Partial least squares Regression models with leave one out cross validation
plsR.RdThis function implements Partial least squares Regression models with leave one out cross validation for complete or incomplete datasets.
Usage
plsR(object, ...)
# Default S3 method
plsRmodel(object, dataX, nt = 2, limQ2set = 0.0975,
dataPredictY = dataX, modele = "pls", family = NULL, typeVC = "none",
EstimXNA = FALSE, scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE,
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12), weights,
sparse = FALSE, sparseStop = TRUE, naive = FALSE,verbose=TRUE,...)
# S3 method for class 'formula'
plsRmodel(object, data, nt = 2, limQ2set = 0.0975,
dataPredictY, modele = "pls", family = NULL, typeVC = "none",
EstimXNA = FALSE, scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE,
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12), weights,
subset, contrasts = NULL, sparse = FALSE, sparseStop = TRUE, naive = FALSE,
verbose=TRUE,...)
PLS_lm(dataY, dataX, nt = 2, limQ2set = 0.0975, dataPredictY = dataX,
modele = "pls", family = NULL, typeVC = "none", EstimXNA = FALSE,
scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE,
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12),
weights,sparse=FALSE,sparseStop=FALSE,naive=FALSE,verbose=TRUE)
PLS_lm_formula(formula,data=NULL,nt=2,limQ2set=.0975,dataPredictY=dataX,
modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,scaleX=TRUE,
scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,MClassed=FALSE,
tol_Xi=10^(-12),weights,subset,contrasts=NULL,sparse=FALSE,
sparseStop=FALSE,naive=FALSE,verbose=TRUE)Arguments
- object
response (training) dataset or an object of class "
formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.- dataY
response (training) dataset
- dataX
predictor(s) (training) dataset
- formula
an object of class "
formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.- data
an optional data frame, list or environment (or object coercible by
as.data.frameto a data frame) containing the variables in the model. If not found indata, the variables are taken fromenvironment(formula), typically the environment from whichplsRis called.- nt
number of components to be extracted
- limQ2set
limit value for the Q2
- dataPredictY
predictor(s) (testing) dataset
- modele
name of the PLS model to be fitted, only (
"pls"available for this fonction.- family
for the present moment the family argument is ignored and set thanks to the value of modele.
- typeVC
type of leave one out cross validation. Several procedures are available. If cross validation is required, one needs to selects the way of predicting the response for left out observations. For complete rows, without any missing value, there are two different ways of computing these predictions. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (
missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).noneno cross validation
standardas in SIMCA for datasets without any missing value. For datasets with any missing value, it is the as using
missingdatamissingdataall values predicted as those with missing values for datasets with any missing values
adaptativepredict a response value for an x with any missing value as those with missing values and for an x without any missing value as those without missing values.
- EstimXNA
only for
modele="pls". Set whether the missing X values have to be estimated.- scaleX
scale the predictor(s) : must be set to TRUE for
modele="pls"and should be for glms pls.- scaleY
scale the response : Yes/No. Ignored since non always possible for glm responses.
- pvals.expli
should individual p-values be reported to tune model selection ?
- alpha.pvals.expli
level of significance for predictors when pvals.expli=TRUE
- MClassed
number of missclassified cases, should only be used for binary responses
- tol_Xi
minimal value for Norm2(Xi) and \(\mathrm{det}(pp' \times pp)\) if there is any missing value in the
dataX. It defaults to \(10^{-12}\)- weights
an optional vector of 'prior weights' to be used in the fitting process. Should be
NULLor a numeric vector.- subset
an optional vector specifying a subset of observations to be used in the fitting process.
- contrasts
an optional list. See the
contrasts.argofmodel.matrix.default.- sparse
should the coefficients of non-significant predictors (<
alpha.pvals.expli) be set to 0- sparseStop
should component extraction stop when no significant predictors (<
alpha.pvals.expli) are found- naive
Use the naive estimates for the Degrees of Freedom in plsR? Default is
FALSE.- verbose
should info messages be displayed ?
- ...
arguments to pass to
plsRmodel.defaultor toplsRmodel.formula
Details
There are several ways to deal with missing values that leads to different computations of leave one out cross validation criteria.
A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.
A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.
Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.
The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one. Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see N. Kraemer and M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705, 2011.
Value
- nr
Number of observations
- nc
Number of predictors
- nt
Number of requested components
- ww
raw weights (before L2-normalization)
- wwnorm
L2 normed weights (to be used with deflated matrices of predictor variables)
- wwetoile
modified weights (to be used with original matrix of predictor variables)
- tt
PLS components
- pp
loadings of the predictor variables
- CoeffC
coefficients of the PLS components
- uscores
scores of the response variable
- YChapeau
predicted response values for the dataX set
- residYChapeau
residuals of the deflated response on the standardized scale
- RepY
scaled response vector
- na.miss.Y
is there any NA value in the response vector
- YNA
indicatrix vector of missing values in RepY
- residY
deflated scaled response vector
- ExpliX
scaled matrix of predictors
- na.miss.X
is there any NA value in the predictor matrix
- XXNA
indicator of non-NA values in the predictor matrix
- residXX
deflated predictor matrix
- PredictY
response values with NA replaced with 0
- press.ind
individual PRESS value for each observation (scaled scale)
- press.tot
total PRESS value for all observations (scaled scale)
- family
glm family used to fit PLSGLR model
- ttPredictY
PLS components for the dataset on which prediction was requested
- typeVC
type of leave one out cross-validation used
- dataX
predictor values
- dataY
response values
- computed_nt
number of components that were computed
- CoeffCFull
matrix of the coefficients of the predictors
- CoeffConstante
value of the intercept (scaled scale)
- Std.Coeffs
Vector of standardized regression coefficients
- press.ind2
individual PRESS value for each observation (original scale)
- RSSresidY
residual sum of squares (scaled scale)
- Coeffs
Vector of regression coefficients (used with the original data scale)
- Yresidus
residuals of the PLS model
- RSS
residual sum of squares (original scale)
- residusY
residuals of the deflated response on the standardized scale
- AIC.std
AIC.std vs number of components (AIC computed for the standardized model
- AIC
AIC vs number of components
- optional
If the response is assumed to be binary:
i.e.MClassed=TRUE.MissClassedNumber of miss classed results
Probs"Probability" predicted by the model. These are not true probabilities since they may lay outside of [0,1]
Probs.trcProbability predicted by the model and constrained to belong to [0,1]
- ttPredictFittedMissingY
Description of 'comp2'
- optional
If cross validation was requested:
i.e.typeVC="standard",typeVC="missingdata"ortypeVC="adaptative".R2residYR2 coefficient value on the standardized scale
R2R2 coefficient value on the original scale
press.tot2total PRESS value for all observations (original scale)
Q2Q2 value (standardized scale)
limQ2limit of the Q2 value
Q2_2Q2 value (original scale)
Q2cumcumulated Q2 (standardized scale)
Q2cum_2cumulated Q2 (original scale)
- InfCrit
table of Information Criteria
- Std.ValsPredictY
predicted response values for supplementary dataset (standardized scale)
- ValsPredictY
predicted response values for supplementary dataset (original scale)
- Std.XChapeau
estimated values for missing values in the predictor matrix (standardized scale)
- XXwotNA
predictor matrix with missing values replaced with 0
References
Nicolas Meyer, Myriam Maumy-Bertrand et Frederic Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/
Author
Frederic Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
See also
See also plsRglm to fit PLSGLR models.
Examples
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
#maximum 6 components could be extracted from this dataset
#trying 10 to trigger automatic stopping criterion
modpls10<-plsR(yCornell,XCornell,10)
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
modpls10
#> Number of required components:
#> [1] 10
#> Number of successfully computed components:
#> [1] 6
#> Coefficients:
#> [,1]
#> Intercept 88.7107982
#> X1 -54.3905712
#> X2 -2.7879678
#> X3 52.5411315
#> X4 -11.5306977
#> X5 -0.9605822
#> X6 11.5900307
#> X7 28.2104803
#> Information criteria and Fit statistics:
#> AIC RSS_Y R2_Y R2_residY RSS_residY AIC.std
#> Nb_Comp_0 82.01205 467.796667 NA NA 11.00000000 37.010388
#> Nb_Comp_1 53.15173 35.742486 0.9235940 0.9235940 0.84046633 8.150064
#> Nb_Comp_2 41.08283 11.066606 0.9763431 0.9763431 0.26022559 -3.918831
#> Nb_Comp_3 32.06411 4.418081 0.9905556 0.9905556 0.10388893 -12.937550
#> Nb_Comp_4 33.76477 4.309235 0.9907882 0.9907882 0.10132947 -11.236891
#> Nb_Comp_5 33.34373 3.521924 0.9924713 0.9924713 0.08281624 -11.657929
#> Nb_Comp_6 35.25533 3.496074 0.9925265 0.9925265 0.08220840 -9.746328
#> DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof DoF.naive
#> Nb_Comp_0 1.000000 6.5212706 46.0708838 47.7893514 27.59461 1
#> Nb_Comp_1 2.740749 1.8665281 4.5699686 4.9558156 21.34020 2
#> Nb_Comp_2 5.085967 1.1825195 2.1075461 2.3949331 27.40202 3
#> Nb_Comp_3 5.121086 0.7488308 0.8467795 0.9628191 24.40842 4
#> Nb_Comp_4 5.103312 0.7387162 0.8232505 0.9357846 24.23105 5
#> Nb_Comp_5 6.006316 0.7096382 0.7976101 0.9198348 28.21184 6
#> Nb_Comp_6 7.000002 0.7633343 0.9711322 1.1359502 33.18348 7
#> sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 0.8361907 1.1070902 1.3048716 33.63927
#With iterated leave one out CV PRESS
modpls6cv<-plsR(Y~.,data=Cornell,6,typeVC="standard")
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
modpls6cv
#> Number of required components:
#> [1] 6
#> Number of successfully computed components:
#> [1] 6
#> Coefficients:
#> [,1]
#> Intercept 88.7107982
#> X1 -54.3905712
#> X2 -2.7879678
#> X3 52.5411315
#> X4 -11.5306977
#> X5 -0.9605822
#> X6 11.5900307
#> X7 28.2104803
#> Leave one out cross validated PRESS, Information criteria and Fit statistics:
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8966556 0.0975 0.89665563 48.344150 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.9175426 0.0975 0.20210989 28.518576 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.9399676 0.0975 0.27195907 8.056942 4.418081 0.9905556
#> Nb_Comp_4 33.76477 0.9197009 0.0975 -0.33759604 5.909608 4.309235 0.9907882
#> Nb_Comp_5 33.34373 0.9281373 0.0975 0.10506161 3.856500 3.521924 0.9924713
#> Nb_Comp_6 35.25533 0.9232562 0.0975 -0.06792167 3.761138 3.496074 0.9925265
#> R2_residY RSS_residY PRESS_residY Q2_residY LimQ2 Q2cum_residY
#> Nb_Comp_0 NA 11.00000000 NA NA NA NA
#> Nb_Comp_1 0.9235940 0.84046633 1.13678803 0.89665563 0.0975 0.8966556
#> Nb_Comp_2 0.9763431 0.26022559 0.67059977 0.20210989 0.0975 0.9175426
#> Nb_Comp_3 0.9905556 0.10388893 0.18945488 0.27195907 0.0975 0.9399676
#> Nb_Comp_4 0.9907882 0.10132947 0.13896142 -0.33759604 0.0975 0.9197009
#> Nb_Comp_5 0.9924713 0.08281624 0.09068364 0.10506161 0.0975 0.9281373
#> Nb_Comp_6 0.9925265 0.08220840 0.08844125 -0.06792167 0.0975 0.9232562
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
cv.modpls<-cv.plsR(Y~.,data=Cornell,6,NK=100, verbose=FALSE)
res.cv.modpls<-cvtable(summary(cv.modpls))
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
#> NK: 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
#> NK: 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
#> NK: 31, 32, 33, 34, 35, 36, 37, 38, 39, 40
#> NK: 41, 42, 43, 44, 45, 46, 47, 48, 49, 50
#> NK: 51, 52, 53, 54, 55, 56, 57, 58, 59, 60
#> NK: 61, 62, 63, 64, 65, 66, 67, 68, 69, 70
#> NK: 71, 72, 73, 74, 75, 76, 77, 78, 79, 80
#> NK: 81, 82, 83, 84, 85, 86, 87, 88, 89, 90
#> NK: 91, 92, 93, 94, 95, 96, 97, 98, 99, 100
#>
#> CV Q2 criterion:
#> 0 1 2
#> 0 86 14
#>
#> CV Press criterion:
#> 1 2 3 4 5
#> 0 1 31 52 16
plot(res.cv.modpls)
rm(list=c("XCornell","yCornell","modpls10","modpls6cv"))
# \donttest{
#A binary response example
data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
modpls.aze <- plsR(yaze_compl,Xaze_compl,10,MClassed=TRUE,typeVC="standard")
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
modpls.aze
#> Number of required components:
#> [1] 10
#> Number of successfully computed components:
#> [1] 10
#> Coefficients:
#> [,1]
#> Intercept 0.308019808
#> D2S138 -0.131218617
#> D18S61 0.450219840
#> D16S422 -0.183848373
#> D17S794 0.269084083
#> D6S264 0.105061098
#> D14S65 -0.052837918
#> D18S53 0.008489326
#> D17S790 -0.213122117
#> D1S225 0.046277290
#> D3S1282 -0.095666162
#> D9S179 0.054547887
#> D5S430 -0.126491043
#> D8S283 0.106373432
#> D11S916 0.111623381
#> D2S159 0.056759714
#> D16S408 0.010288859
#> D5S346 0.233674850
#> D10S191 0.010715856
#> D13S173 0.074148740
#> D6S275 -0.123145693
#> D15S127 0.064566148
#> D1S305 0.190500469
#> D4S394 -0.142585807
#> D20S107 -0.184483600
#> D1S197 -0.284373695
#> D1S207 0.186728597
#> D10S192 0.195516079
#> D3S1283 -0.096309755
#> D4S414 0.017960975
#> D8S264 0.121051206
#> D22S928 -0.049091794
#> TP53 -0.391965015
#> D9S171 -0.012315197
#> Leave one out cross validated PRESS, Information criteria and Fit statistics:
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 154.6179 NA NA NA NA 25.91346
#> Nb_Comp_1 126.4083 -0.09840016 0.0975 -0.09840016 28.46335 19.38086
#> Nb_Comp_2 119.3375 -0.19018163 0.0975 -0.08355923 21.00031 17.76209
#> Nb_Comp_3 114.2313 -0.77332918 0.0975 -0.48996518 26.46489 16.58896
#> Nb_Comp_4 112.3463 -1.64635954 0.0975 -0.49231150 24.75590 15.98071
#> Nb_Comp_5 113.2362 -2.74242209 0.0975 -0.41417749 22.59955 15.81104
#> Nb_Comp_6 114.7620 -4.46009228 0.0975 -0.45897286 23.06788 15.73910
#> Nb_Comp_7 116.5264 -7.36664482 0.0975 -0.53232663 24.11744 15.70350
#> Nb_Comp_8 118.4601 -11.80011367 0.0975 -0.52989806 24.02475 15.69348
#> Nb_Comp_9 120.4452 -17.90787273 0.0975 -0.47716444 23.18185 15.69123
#> Nb_Comp_10 122.4395 -26.50536212 0.0975 -0.45470421 22.82610 15.69037
#> R2_Y MissClassed R2_residY RSS_residY PRESS_residY Q2_residY
#> Nb_Comp_0 NA 49 NA 103.00000 NA NA
#> Nb_Comp_1 0.2520929 27 0.2520929 77.03443 113.13522 -0.09840016
#> Nb_Comp_2 0.3145613 25 0.3145613 70.60018 83.47137 -0.08355923
#> Nb_Comp_3 0.3598323 27 0.3598323 65.93728 105.19181 -0.48996518
#> Nb_Comp_4 0.3833049 23 0.3833049 63.51960 98.39895 -0.49231150
#> Nb_Comp_5 0.3898523 22 0.3898523 62.84522 89.82798 -0.41417749
#> Nb_Comp_6 0.3926285 21 0.3926285 62.55927 91.68947 -0.45897286
#> Nb_Comp_7 0.3940024 20 0.3940024 62.41775 95.86123 -0.53232663
#> Nb_Comp_8 0.3943888 20 0.3943888 62.37795 95.49280 -0.52989806
#> Nb_Comp_9 0.3944758 19 0.3944758 62.36900 92.14249 -0.47716444
#> Nb_Comp_10 0.3945088 19 0.3945088 62.36560 90.72844 -0.45470421
#> LimQ2 Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof
#> Nb_Comp_0 NA NA 298.1344 1.00000 0.5015845 0.2540061
#> Nb_Comp_1 0.0975 -0.09840016 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 0.0975 -0.19018163 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 0.0975 -0.77332918 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 0.0975 -1.64635954 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 0.0975 -2.74242209 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 0.0975 -4.46009228 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 0.0975 -7.36664482 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 0.0975 -11.80011367 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 0.0975 -17.90787273 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 0.0975 -26.50536212 265.9560 34.00000 0.4700970 0.2953632
#> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive
#> Nb_Comp_0 0.2604032 -67.17645 1 0.5015845 0.2540061 0.2604032
#> Nb_Comp_1 0.4231184 -53.56607 2 0.4358996 0.1936625 0.2033251
#> Nb_Comp_2 0.4496983 -52.42272 3 0.4193593 0.1809352 0.1943501
#> Nb_Comp_3 0.4631316 -51.93343 4 0.4072955 0.1722700 0.1891422
#> Nb_Comp_4 0.4954133 -50.37079 5 0.4017727 0.1691819 0.1897041
#> Nb_Comp_5 0.4901536 -50.65724 6 0.4016679 0.1706451 0.1952588
#> Nb_Comp_6 0.4879234 -50.78005 7 0.4028135 0.1731800 0.2020601
#> Nb_Comp_7 0.4826103 -51.05525 8 0.4044479 0.1761610 0.2094352
#> Nb_Comp_8 0.4865092 -50.85833 9 0.4064413 0.1794902 0.2172936
#> Nb_Comp_9 0.4845867 -50.95616 10 0.4085682 0.1829787 0.2254232
#> Nb_Comp_10 0.4864128 -50.86368 11 0.4107477 0.1865584 0.2337468
#> GMDL.naive
#> Nb_Comp_0 -67.17645
#> Nb_Comp_1 -79.67755
#> Nb_Comp_2 -81.93501
#> Nb_Comp_3 -83.31503
#> Nb_Comp_4 -83.23369
#> Nb_Comp_5 -81.93513
#> Nb_Comp_6 -80.42345
#> Nb_Comp_7 -78.87607
#> Nb_Comp_8 -77.31942
#> Nb_Comp_9 -75.80069
#> Nb_Comp_10 -74.33325
#Direct access to not cross-validated values
modpls.aze$AIC
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 154.6179 126.4083 119.3375 114.2313 112.3463 113.2362 114.762 116.5264
#> [,9] [,10] [,11]
#> [1,] 118.4601 120.4452 122.4395
modpls.aze$AIC.std
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 298.1344 269.9248 262.854 257.7478 255.8628 256.7527 258.2785 260.0429
#> [,9] [,10] [,11]
#> [1,] 261.9766 263.9617 265.956
modpls.aze$MissClassed
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
#> [1,] 49 27 25 27 23 22 21 20 20 19 19
#Raw predicted values (not really probabily since not constrained in [0,1]
modpls.aze$Probs
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0.4711538 0.46105744 0.63458141 0.67961627 0.69452246 0.64534767
#> 2 0.4711538 0.26911816 0.26581497 0.16989268 0.11760783 0.18096700
#> 3 0.4711538 -0.09080494 -0.05104846 -0.17166916 -0.21455242 -0.21725391
#> 4 0.4711538 0.36370490 0.54112657 0.50724821 0.55508565 0.57773785
#> 5 0.4711538 -0.04408124 0.07399231 -0.07129909 -0.24018962 -0.23445282
#> 6 0.4711538 -0.03776963 0.17275288 0.01806190 -0.02597539 -0.06284454
#> 7 0.4711538 -0.06930728 -0.19928456 -0.09137261 0.01116043 0.06506517
#> 8 0.4711538 0.27158233 0.24933653 0.11611522 0.12804487 0.04118115
#> 9 0.4711538 0.76949497 0.60296556 0.47237794 0.51581382 0.49885092
#> 10 0.4711538 0.22096539 0.34482052 0.34660816 0.38580378 0.43528451
#> 11 0.4711538 0.87147914 0.84865348 0.76372713 0.73582307 0.76725258
#> 12 0.4711538 0.79792975 0.67828859 0.73747065 0.67844373 0.67908585
#> 13 0.4711538 0.09432664 -0.04344681 0.10780023 0.22488457 0.26144110
#> 14 0.4711538 0.28543133 0.29293086 0.37385135 0.37961001 0.30207755
#> 15 0.4711538 0.30637401 0.27816310 0.18074751 0.01510565 0.05074255
#> 16 0.4711538 0.12893721 -0.07276258 -0.05146556 -0.09988241 -0.06790398
#> 17 0.4711538 0.59910292 0.41302582 0.40055026 0.32477692 0.32429673
#> 18 0.4711538 0.60665328 0.51461671 0.70351041 0.63093215 0.60232625
#> 19 0.4711538 0.18381206 0.36596047 0.33591603 0.25289460 0.21859872
#> 20 0.4711538 0.28422822 0.15202852 0.29980632 0.42075827 0.43463142
#> 21 0.4711538 0.35982960 0.40300075 0.63220247 0.58056075 0.55273462
#> 22 0.4711538 0.31574837 0.28422517 0.37116719 0.27156145 0.25529246
#> 23 0.4711538 0.41682757 0.36900849 0.23791176 0.25730930 0.24221472
#> 24 0.4711538 0.30288056 0.15972272 0.19362318 0.07194768 0.07250435
#> 25 0.4711538 0.29650015 0.48867070 0.61025747 0.59737342 0.67704212
#> 26 0.4711538 0.23008536 0.32001822 0.15862645 0.26312675 0.22513847
#> 27 0.4711538 0.67526360 0.68123526 0.58796740 0.51309143 0.44381568
#> 28 0.4711538 0.15222775 0.13544964 0.15605402 0.15868232 0.10574096
#> 29 0.4711538 0.43138914 0.29576924 0.29706087 0.35294305 0.40257625
#> 30 0.4711538 0.13910581 0.26763382 0.10182481 0.12169881 0.13543560
#> 31 0.4711538 0.40295972 0.43810789 0.28684877 0.41632594 0.45388666
#> 32 0.4711538 0.58422149 0.44366239 0.16615851 0.15367980 0.18291151
#> 33 0.4711538 0.69889100 0.72592310 0.57845537 0.50185886 0.51841164
#> 34 0.4711538 0.35960908 0.24234167 0.09364940 0.08428214 0.10528276
#> 35 0.4711538 0.27914959 0.03731133 -0.08896074 -0.06232370 -0.08231459
#> 36 0.4711538 0.38865989 0.39024480 0.44138316 0.47508801 0.42329842
#> 37 0.4711538 0.62200134 0.42145828 0.38142396 0.29675933 0.28947211
#> 38 0.4711538 0.41311694 0.19970983 0.16702613 0.17059545 0.17073272
#> 39 0.4711538 0.31755422 0.28395547 0.17609314 0.23875966 0.25763504
#> 40 0.4711538 0.62628933 0.51627261 0.52025889 0.47789760 0.47304606
#> 41 0.4711538 0.14894845 0.14069540 0.13906223 0.05976750 0.13670893
#> 42 0.4711538 0.64041121 0.49727655 0.49380105 0.53239359 0.51394469
#> 43 0.4711538 0.38696544 0.54930653 0.62650411 0.65244562 0.56755351
#> 44 0.4711538 0.24204195 0.05825611 0.02230584 -0.01790809 -0.03785626
#> 45 0.4711538 0.10349021 0.14957660 0.16304594 0.15564790 0.17065395
#> 46 0.4711538 0.63322787 0.64625855 0.55541948 0.65203351 0.63670168
#> 47 0.4711538 0.20557889 0.23864853 0.24328712 0.13063078 0.09743813
#> 48 0.4711538 0.32352238 0.34894312 0.21162810 0.20487572 0.16461876
#> 49 0.4711538 0.64888519 0.52290405 0.50926772 0.62061797 0.59597941
#> 50 0.4711538 0.44153005 0.49754241 0.32749149 0.24840605 0.32456388
#> 51 0.4711538 0.32562433 0.23887414 0.26764033 0.24950898 0.30432045
#> 52 0.4711538 -0.23250098 -0.28713647 -0.09216174 -0.12709475 -0.18324647
#> 53 0.4711538 0.53388610 0.47710127 0.60836140 0.48273912 0.43334108
#> 54 0.4711538 0.64191356 0.44931093 0.46371798 0.45275305 0.46653696
#> 55 0.4711538 0.05279255 0.06829351 0.15306458 0.25200214 0.21249173
#> 56 0.4711538 0.59808020 0.64333345 0.53741245 0.64108173 0.57876914
#> 57 0.4711538 0.53093147 0.62138656 0.92046148 0.93004391 0.95130430
#> 58 0.4711538 0.64943097 0.57141374 0.66800038 0.64835800 0.65566321
#> 59 0.4711538 0.42541400 0.43027409 0.30117492 0.36183156 0.29992796
#> 60 0.4711538 0.24537249 0.29963849 0.42931558 0.51048830 0.58927966
#> 61 0.4711538 0.64269314 0.62785202 0.75163561 0.68045267 0.67000184
#> 62 0.4711538 0.51277761 0.60877778 0.75493489 0.66735142 0.63862193
#> 63 0.4711538 0.53377378 0.53228159 0.56245626 0.58414332 0.61176055
#> 64 0.4711538 0.79099666 0.90572246 0.92244949 0.93001276 0.93454809
#> 65 0.4711538 0.73768777 0.61339931 0.72362105 0.70536287 0.69970096
#> 66 0.4711538 0.70767466 0.53408924 0.50675818 0.52181506 0.54559559
#> 67 0.4711538 0.96312042 1.17012215 1.08116795 1.22497425 1.21728258
#> 68 0.4711538 0.31575995 0.57179559 0.77297374 0.78532935 0.78484987
#> 69 0.4711538 0.69505872 0.78176548 0.74300700 0.72711033 0.70750770
#> 70 0.4711538 0.72276362 0.90232185 0.89364576 0.84428623 0.92659977
#> 71 0.4711538 0.50950893 0.39503961 0.45591683 0.38297596 0.35086204
#> 72 0.4711538 0.14720074 0.13538571 -0.04473829 -0.05529233 0.02748516
#> 73 0.4711538 0.49275110 0.44937896 0.41856171 0.62470016 0.61654596
#> 74 0.4711538 0.65674324 0.69439259 0.75479685 0.88511667 0.92560996
#> 75 0.4711538 0.68716407 0.57541914 0.59945962 0.54581071 0.55228791
#> 76 0.4711538 0.54839542 0.50508123 0.52627725 0.55765709 0.52543838
#> 77 0.4711538 0.77317727 0.79812663 0.93073165 1.10301473 1.08723742
#> 78 0.4711538 0.85322027 0.76128342 0.81061207 0.85796753 0.87947603
#> 79 0.4711538 0.81659194 0.90228252 0.80744839 0.70383361 0.68468090
#> 80 0.4711538 0.55964651 0.44326524 0.39507689 0.36149039 0.32071350
#> 81 0.4711538 0.87105473 0.86695796 0.89177640 0.74816339 0.69831750
#> 82 0.4711538 0.47715869 0.68930595 0.71280202 0.73606020 0.78321326
#> 83 0.4711538 0.80974821 0.87138779 0.97466313 0.93082943 0.95560886
#> 84 0.4711538 0.67739807 0.85743609 0.98894432 0.96011041 0.90800271
#> 85 0.4711538 0.57131444 0.34250950 0.33855791 0.31118498 0.31383288
#> 86 0.4711538 0.84958765 0.97611051 0.93090902 0.91560248 0.86222031
#> 87 0.4711538 0.57644613 0.41449248 0.48714466 0.54811918 0.57041511
#> 88 0.4711538 0.75932310 0.71214369 0.52234742 0.59011684 0.59023780
#> 89 0.4711538 0.53031516 0.47090892 0.42433053 0.38847912 0.39218094
#> 90 0.4711538 0.76770402 1.07649866 1.00864429 1.06363018 1.09017457
#> 91 0.4711538 0.38643842 0.37696993 0.44452861 0.49450298 0.46628856
#> 92 0.4711538 0.92591633 1.03707888 0.96084369 0.95688931 0.93400393
#> 93 0.4711538 0.66726042 0.89247800 0.87390628 0.87335977 0.95801535
#> 94 0.4711538 0.32634752 0.41373057 0.48066349 0.67273089 0.62115180
#> 95 0.4711538 0.50472276 0.77159222 0.71730564 0.62350221 0.64335334
#> 96 0.4711538 0.34622269 0.33150717 0.49412629 0.44574013 0.46889514
#> 97 0.4711538 0.55805257 0.50280611 0.58541977 0.52239953 0.53556273
#> 98 0.4711538 0.78090964 0.73429355 0.79385683 0.86651416 0.88151677
#> 99 0.4711538 0.21116352 0.10917861 0.02565398 0.18342015 0.15222876
#> 100 0.4711538 0.66672702 0.78264411 0.86306662 0.75733969 0.77632472
#> 101 0.4711538 0.45317545 0.50149615 0.62617428 0.70904267 0.78134354
#> 102 0.4711538 0.74435376 0.66135006 0.72568147 0.70203564 0.77593538
#> 103 0.4711538 0.34690226 0.56605434 0.52782336 0.50951738 0.46795757
#> 104 0.4711538 0.69496014 0.80515138 0.78871059 0.78008789 0.78042831
#> [,7] [,8] [,9] [,10] [,11]
#> 1 0.64037279 0.627340571 0.651243676 0.65354280 0.65838797
#> 2 0.21304385 0.202666528 0.206463548 0.21046038 0.20470356
#> 3 -0.22444089 -0.193652144 -0.201652437 -0.20167289 -0.20081532
#> 4 0.58413761 0.600377920 0.608768219 0.60784745 0.60193905
#> 5 -0.26327049 -0.311781941 -0.310765976 -0.31066830 -0.31401382
#> 6 -0.10341096 -0.076840858 -0.080016598 -0.08436785 -0.08777135
#> 7 0.10261786 0.132750517 0.144750243 0.13944940 0.14173177
#> 8 0.04809780 0.063736599 0.063261540 0.07570783 0.07715150
#> 9 0.44543808 0.444943670 0.447021225 0.44582742 0.44748513
#> 10 0.43490588 0.407005593 0.413663760 0.41349216 0.41350336
#> 11 0.78695284 0.777623618 0.783734315 0.78262765 0.77949531
#> 12 0.65654818 0.642154505 0.633436560 0.63197252 0.62875264
#> 13 0.21708341 0.187509114 0.186533541 0.17822088 0.17842513
#> 14 0.28651410 0.260501290 0.279081223 0.27196942 0.26996735
#> 15 0.05784774 0.095949877 0.090894676 0.08865039 0.09080522
#> 16 -0.06239212 -0.039505632 -0.023469898 -0.02045198 -0.02715190
#> 17 0.33482409 0.336193547 0.335468481 0.33501592 0.33670840
#> 18 0.59257402 0.580678556 0.581449676 0.58013547 0.57927640
#> 19 0.24272344 0.246701307 0.240991178 0.23822863 0.23157123
#> 20 0.40555564 0.386074751 0.400419290 0.40843493 0.40860630
#> 21 0.53582205 0.549336181 0.539598759 0.54139679 0.54098845
#> 22 0.27214775 0.265323031 0.262889367 0.27124503 0.27372049
#> 23 0.26310951 0.254264842 0.244111736 0.24044626 0.24169731
#> 24 0.10789143 0.136298989 0.142908568 0.14863967 0.15404679
#> 25 0.62754156 0.650367844 0.644944061 0.64644697 0.64416177
#> 26 0.18477380 0.190402191 0.199245619 0.20152378 0.20582432
#> 27 0.44358238 0.454283166 0.446210009 0.43311399 0.43568840
#> 28 0.13769251 0.118081488 0.117589648 0.11048814 0.11155803
#> 29 0.42759376 0.424018002 0.417256036 0.42362830 0.42160066
#> 30 0.10952676 0.168692282 0.174041713 0.17387211 0.17480429
#> 31 0.45876287 0.432435790 0.425259491 0.43329412 0.43856907
#> 32 0.11742399 0.116981896 0.108778824 0.10636129 0.10689920
#> 33 0.47697376 0.450738829 0.452062165 0.44881973 0.44992624
#> 34 0.09863470 0.095862956 0.092253997 0.09980718 0.10084230
#> 35 -0.08390506 -0.085155814 -0.086690885 -0.08262604 -0.08272575
#> 36 0.45381739 0.433667887 0.449594388 0.44854107 0.44960414
#> 37 0.30022320 0.311627620 0.315020647 0.31958491 0.32067580
#> 38 0.15959719 0.160997289 0.151222415 0.15079738 0.14817778
#> 39 0.27800144 0.253643110 0.255071837 0.25875102 0.25674198
#> 40 0.51555833 0.530347073 0.528402618 0.52790558 0.52538704
#> 41 0.09275154 0.101869980 0.090604733 0.09445259 0.09150467
#> 42 0.50207194 0.483136866 0.487491071 0.48860245 0.49212211
#> 43 0.57336835 0.573091866 0.546609274 0.54001848 0.54366888
#> 44 -0.01451487 -0.004369241 -0.002647953 -0.00844687 -0.01073887
#> 45 0.19657318 0.164988597 0.170522098 0.16781405 0.16715155
#> 46 0.65287559 0.645084587 0.651535848 0.65177845 0.65178893
#> 47 0.12452036 0.119913760 0.117053912 0.11873700 0.11591426
#> 48 0.18275993 0.166998459 0.160605969 0.16869180 0.16855238
#> 49 0.58006784 0.613009102 0.608873759 0.60785525 0.61021103
#> 50 0.33208894 0.314978063 0.311974842 0.30517646 0.31174053
#> 51 0.32816749 0.318321881 0.320797741 0.31281691 0.31540371
#> 52 -0.19584259 -0.184768370 -0.177691131 -0.18225253 -0.18172040
#> 53 0.40794213 0.415136651 0.416044038 0.42408314 0.42075636
#> 54 0.46480524 0.479688395 0.471302624 0.46613648 0.46656474
#> 55 0.23097197 0.213772954 0.189358085 0.18930563 0.19050731
#> 56 0.57331393 0.574558380 0.579775660 0.57922184 0.57800858
#> 57 0.96183872 0.975414458 0.968855613 0.96385549 0.96028958
#> 58 0.64361788 0.631632631 0.641013340 0.63785489 0.64045306
#> 59 0.28643229 0.289148607 0.278791345 0.28784917 0.28782462
#> 60 0.55978204 0.559023840 0.561630138 0.55671889 0.55726966
#> 61 0.65787202 0.659412106 0.650588243 0.64930310 0.65091617
#> 62 0.60705201 0.603977438 0.589826600 0.58812271 0.58793743
#> 63 0.63813827 0.617717657 0.614454772 0.61166047 0.60985403
#> 64 1.00166279 1.008968255 1.008852814 1.01022555 1.00666729
#> 65 0.69297263 0.697694744 0.689810628 0.69214943 0.69038662
#> 66 0.53663407 0.532369158 0.535934708 0.53171176 0.53102146
#> 67 1.22111150 1.202569147 1.194030443 1.19310867 1.19228184
#> 68 0.80071187 0.812462410 0.796883689 0.80360414 0.80689017
#> 69 0.74791867 0.774669023 0.764357440 0.76435231 0.76730276
#> 70 0.93952180 0.926356875 0.916496158 0.91637035 0.92383169
#> 71 0.31179970 0.319584982 0.342996678 0.34334588 0.34349096
#> 72 0.08069763 0.064883087 0.053282640 0.04500807 0.04571864
#> 73 0.63914960 0.656024336 0.647841400 0.64971036 0.64930911
#> 74 0.94540783 0.945531101 0.947433351 0.95548576 0.95086945
#> 75 0.56609663 0.581051527 0.586510546 0.58350518 0.58195688
#> 76 0.49807985 0.507124137 0.503700114 0.50352891 0.50289031
#> 77 1.06463806 1.090636226 1.099163425 1.09692704 1.09684987
#> 78 0.88947890 0.914074686 0.921993535 0.92611779 0.92473772
#> 79 0.70672170 0.689699520 0.693322761 0.69306283 0.69031633
#> 80 0.30181332 0.296560012 0.282907508 0.28672758 0.28334638
#> 81 0.69871492 0.692603330 0.692711040 0.69448149 0.69353087
#> 82 0.73754433 0.755515607 0.753806256 0.76075515 0.76006115
#> 83 0.95554583 0.981114506 0.967899112 0.96789876 0.97167928
#> 84 0.92460814 0.928255751 0.934569121 0.93157477 0.93560641
#> 85 0.31932547 0.326702214 0.334191767 0.33780085 0.33863677
#> 86 0.82950069 0.806588554 0.823792983 0.82202715 0.81981455
#> 87 0.56750119 0.560439488 0.549720853 0.54609905 0.54656224
#> 88 0.57484476 0.588841816 0.582284530 0.57334028 0.57098492
#> 89 0.44028895 0.455790854 0.462215323 0.46149391 0.46635298
#> 90 1.08703587 1.113909884 1.123382027 1.12669615 1.12559691
#> 91 0.50591629 0.528734399 0.542033081 0.53789411 0.54025902
#> 92 0.95078882 0.944122848 0.933909143 0.93429933 0.92818920
#> 93 0.95048831 0.984993815 0.999870929 0.99916201 0.99970721
#> 94 0.63222931 0.617803323 0.604620965 0.60316670 0.59998131
#> 95 0.60173453 0.598838861 0.615665753 0.61062288 0.61003734
#> 96 0.42461082 0.393735123 0.382307431 0.38698306 0.38928062
#> 97 0.55493331 0.545341350 0.548945997 0.55196226 0.55110104
#> 98 0.84875293 0.849088124 0.833445596 0.82896512 0.82882885
#> 99 0.12817884 0.125184677 0.142524174 0.14250387 0.14467219
#> 100 0.76010896 0.740234308 0.744642249 0.75159300 0.75722807
#> 101 0.80270481 0.778624924 0.777761525 0.78102072 0.77766043
#> 102 0.77647730 0.755860583 0.764875857 0.76840548 0.77181270
#> 103 0.50588630 0.488855008 0.495423757 0.50188675 0.50526664
#> 104 0.81071639 0.804180721 0.825464815 0.81861016 0.81635531
#Truncated to [0;1] predicted values (true probabilities)
modpls.aze$Probs.trc
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 0.4711538 0.46105744 0.63458141 0.67961627 0.69452246 0.64534767 0.64037279
#> 2 0.4711538 0.26911816 0.26581497 0.16989268 0.11760783 0.18096700 0.21304385
#> 3 0.4711538 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> 4 0.4711538 0.36370490 0.54112657 0.50724821 0.55508565 0.57773785 0.58413761
#> 5 0.4711538 0.00000000 0.07399231 0.00000000 0.00000000 0.00000000 0.00000000
#> 6 0.4711538 0.00000000 0.17275288 0.01806190 0.00000000 0.00000000 0.00000000
#> 7 0.4711538 0.00000000 0.00000000 0.00000000 0.01116043 0.06506517 0.10261786
#> 8 0.4711538 0.27158233 0.24933653 0.11611522 0.12804487 0.04118115 0.04809780
#> 9 0.4711538 0.76949497 0.60296556 0.47237794 0.51581382 0.49885092 0.44543808
#> 10 0.4711538 0.22096539 0.34482052 0.34660816 0.38580378 0.43528451 0.43490588
#> 11 0.4711538 0.87147914 0.84865348 0.76372713 0.73582307 0.76725258 0.78695284
#> 12 0.4711538 0.79792975 0.67828859 0.73747065 0.67844373 0.67908585 0.65654818
#> 13 0.4711538 0.09432664 0.00000000 0.10780023 0.22488457 0.26144110 0.21708341
#> 14 0.4711538 0.28543133 0.29293086 0.37385135 0.37961001 0.30207755 0.28651410
#> 15 0.4711538 0.30637401 0.27816310 0.18074751 0.01510565 0.05074255 0.05784774
#> 16 0.4711538 0.12893721 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> 17 0.4711538 0.59910292 0.41302582 0.40055026 0.32477692 0.32429673 0.33482409
#> 18 0.4711538 0.60665328 0.51461671 0.70351041 0.63093215 0.60232625 0.59257402
#> 19 0.4711538 0.18381206 0.36596047 0.33591603 0.25289460 0.21859872 0.24272344
#> 20 0.4711538 0.28422822 0.15202852 0.29980632 0.42075827 0.43463142 0.40555564
#> 21 0.4711538 0.35982960 0.40300075 0.63220247 0.58056075 0.55273462 0.53582205
#> 22 0.4711538 0.31574837 0.28422517 0.37116719 0.27156145 0.25529246 0.27214775
#> 23 0.4711538 0.41682757 0.36900849 0.23791176 0.25730930 0.24221472 0.26310951
#> 24 0.4711538 0.30288056 0.15972272 0.19362318 0.07194768 0.07250435 0.10789143
#> 25 0.4711538 0.29650015 0.48867070 0.61025747 0.59737342 0.67704212 0.62754156
#> 26 0.4711538 0.23008536 0.32001822 0.15862645 0.26312675 0.22513847 0.18477380
#> 27 0.4711538 0.67526360 0.68123526 0.58796740 0.51309143 0.44381568 0.44358238
#> 28 0.4711538 0.15222775 0.13544964 0.15605402 0.15868232 0.10574096 0.13769251
#> 29 0.4711538 0.43138914 0.29576924 0.29706087 0.35294305 0.40257625 0.42759376
#> 30 0.4711538 0.13910581 0.26763382 0.10182481 0.12169881 0.13543560 0.10952676
#> 31 0.4711538 0.40295972 0.43810789 0.28684877 0.41632594 0.45388666 0.45876287
#> 32 0.4711538 0.58422149 0.44366239 0.16615851 0.15367980 0.18291151 0.11742399
#> 33 0.4711538 0.69889100 0.72592310 0.57845537 0.50185886 0.51841164 0.47697376
#> 34 0.4711538 0.35960908 0.24234167 0.09364940 0.08428214 0.10528276 0.09863470
#> 35 0.4711538 0.27914959 0.03731133 0.00000000 0.00000000 0.00000000 0.00000000
#> 36 0.4711538 0.38865989 0.39024480 0.44138316 0.47508801 0.42329842 0.45381739
#> 37 0.4711538 0.62200134 0.42145828 0.38142396 0.29675933 0.28947211 0.30022320
#> 38 0.4711538 0.41311694 0.19970983 0.16702613 0.17059545 0.17073272 0.15959719
#> 39 0.4711538 0.31755422 0.28395547 0.17609314 0.23875966 0.25763504 0.27800144
#> 40 0.4711538 0.62628933 0.51627261 0.52025889 0.47789760 0.47304606 0.51555833
#> 41 0.4711538 0.14894845 0.14069540 0.13906223 0.05976750 0.13670893 0.09275154
#> 42 0.4711538 0.64041121 0.49727655 0.49380105 0.53239359 0.51394469 0.50207194
#> 43 0.4711538 0.38696544 0.54930653 0.62650411 0.65244562 0.56755351 0.57336835
#> 44 0.4711538 0.24204195 0.05825611 0.02230584 0.00000000 0.00000000 0.00000000
#> 45 0.4711538 0.10349021 0.14957660 0.16304594 0.15564790 0.17065395 0.19657318
#> 46 0.4711538 0.63322787 0.64625855 0.55541948 0.65203351 0.63670168 0.65287559
#> 47 0.4711538 0.20557889 0.23864853 0.24328712 0.13063078 0.09743813 0.12452036
#> 48 0.4711538 0.32352238 0.34894312 0.21162810 0.20487572 0.16461876 0.18275993
#> 49 0.4711538 0.64888519 0.52290405 0.50926772 0.62061797 0.59597941 0.58006784
#> 50 0.4711538 0.44153005 0.49754241 0.32749149 0.24840605 0.32456388 0.33208894
#> 51 0.4711538 0.32562433 0.23887414 0.26764033 0.24950898 0.30432045 0.32816749
#> 52 0.4711538 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
#> 53 0.4711538 0.53388610 0.47710127 0.60836140 0.48273912 0.43334108 0.40794213
#> 54 0.4711538 0.64191356 0.44931093 0.46371798 0.45275305 0.46653696 0.46480524
#> 55 0.4711538 0.05279255 0.06829351 0.15306458 0.25200214 0.21249173 0.23097197
#> 56 0.4711538 0.59808020 0.64333345 0.53741245 0.64108173 0.57876914 0.57331393
#> 57 0.4711538 0.53093147 0.62138656 0.92046148 0.93004391 0.95130430 0.96183872
#> 58 0.4711538 0.64943097 0.57141374 0.66800038 0.64835800 0.65566321 0.64361788
#> 59 0.4711538 0.42541400 0.43027409 0.30117492 0.36183156 0.29992796 0.28643229
#> 60 0.4711538 0.24537249 0.29963849 0.42931558 0.51048830 0.58927966 0.55978204
#> 61 0.4711538 0.64269314 0.62785202 0.75163561 0.68045267 0.67000184 0.65787202
#> 62 0.4711538 0.51277761 0.60877778 0.75493489 0.66735142 0.63862193 0.60705201
#> 63 0.4711538 0.53377378 0.53228159 0.56245626 0.58414332 0.61176055 0.63813827
#> 64 0.4711538 0.79099666 0.90572246 0.92244949 0.93001276 0.93454809 1.00000000
#> 65 0.4711538 0.73768777 0.61339931 0.72362105 0.70536287 0.69970096 0.69297263
#> 66 0.4711538 0.70767466 0.53408924 0.50675818 0.52181506 0.54559559 0.53663407
#> 67 0.4711538 0.96312042 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
#> 68 0.4711538 0.31575995 0.57179559 0.77297374 0.78532935 0.78484987 0.80071187
#> 69 0.4711538 0.69505872 0.78176548 0.74300700 0.72711033 0.70750770 0.74791867
#> 70 0.4711538 0.72276362 0.90232185 0.89364576 0.84428623 0.92659977 0.93952180
#> 71 0.4711538 0.50950893 0.39503961 0.45591683 0.38297596 0.35086204 0.31179970
#> 72 0.4711538 0.14720074 0.13538571 0.00000000 0.00000000 0.02748516 0.08069763
#> 73 0.4711538 0.49275110 0.44937896 0.41856171 0.62470016 0.61654596 0.63914960
#> 74 0.4711538 0.65674324 0.69439259 0.75479685 0.88511667 0.92560996 0.94540783
#> 75 0.4711538 0.68716407 0.57541914 0.59945962 0.54581071 0.55228791 0.56609663
#> 76 0.4711538 0.54839542 0.50508123 0.52627725 0.55765709 0.52543838 0.49807985
#> 77 0.4711538 0.77317727 0.79812663 0.93073165 1.00000000 1.00000000 1.00000000
#> 78 0.4711538 0.85322027 0.76128342 0.81061207 0.85796753 0.87947603 0.88947890
#> 79 0.4711538 0.81659194 0.90228252 0.80744839 0.70383361 0.68468090 0.70672170
#> 80 0.4711538 0.55964651 0.44326524 0.39507689 0.36149039 0.32071350 0.30181332
#> 81 0.4711538 0.87105473 0.86695796 0.89177640 0.74816339 0.69831750 0.69871492
#> 82 0.4711538 0.47715869 0.68930595 0.71280202 0.73606020 0.78321326 0.73754433
#> 83 0.4711538 0.80974821 0.87138779 0.97466313 0.93082943 0.95560886 0.95554583
#> 84 0.4711538 0.67739807 0.85743609 0.98894432 0.96011041 0.90800271 0.92460814
#> 85 0.4711538 0.57131444 0.34250950 0.33855791 0.31118498 0.31383288 0.31932547
#> 86 0.4711538 0.84958765 0.97611051 0.93090902 0.91560248 0.86222031 0.82950069
#> 87 0.4711538 0.57644613 0.41449248 0.48714466 0.54811918 0.57041511 0.56750119
#> 88 0.4711538 0.75932310 0.71214369 0.52234742 0.59011684 0.59023780 0.57484476
#> 89 0.4711538 0.53031516 0.47090892 0.42433053 0.38847912 0.39218094 0.44028895
#> 90 0.4711538 0.76770402 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
#> 91 0.4711538 0.38643842 0.37696993 0.44452861 0.49450298 0.46628856 0.50591629
#> 92 0.4711538 0.92591633 1.00000000 0.96084369 0.95688931 0.93400393 0.95078882
#> 93 0.4711538 0.66726042 0.89247800 0.87390628 0.87335977 0.95801535 0.95048831
#> 94 0.4711538 0.32634752 0.41373057 0.48066349 0.67273089 0.62115180 0.63222931
#> 95 0.4711538 0.50472276 0.77159222 0.71730564 0.62350221 0.64335334 0.60173453
#> 96 0.4711538 0.34622269 0.33150717 0.49412629 0.44574013 0.46889514 0.42461082
#> 97 0.4711538 0.55805257 0.50280611 0.58541977 0.52239953 0.53556273 0.55493331
#> 98 0.4711538 0.78090964 0.73429355 0.79385683 0.86651416 0.88151677 0.84875293
#> 99 0.4711538 0.21116352 0.10917861 0.02565398 0.18342015 0.15222876 0.12817884
#> 100 0.4711538 0.66672702 0.78264411 0.86306662 0.75733969 0.77632472 0.76010896
#> 101 0.4711538 0.45317545 0.50149615 0.62617428 0.70904267 0.78134354 0.80270481
#> 102 0.4711538 0.74435376 0.66135006 0.72568147 0.70203564 0.77593538 0.77647730
#> 103 0.4711538 0.34690226 0.56605434 0.52782336 0.50951738 0.46795757 0.50588630
#> 104 0.4711538 0.69496014 0.80515138 0.78871059 0.78008789 0.78042831 0.81071639
#> [,8] [,9] [,10] [,11]
#> 1 0.62734057 0.65124368 0.65354280 0.65838797
#> 2 0.20266653 0.20646355 0.21046038 0.20470356
#> 3 0.00000000 0.00000000 0.00000000 0.00000000
#> 4 0.60037792 0.60876822 0.60784745 0.60193905
#> 5 0.00000000 0.00000000 0.00000000 0.00000000
#> 6 0.00000000 0.00000000 0.00000000 0.00000000
#> 7 0.13275052 0.14475024 0.13944940 0.14173177
#> 8 0.06373660 0.06326154 0.07570783 0.07715150
#> 9 0.44494367 0.44702123 0.44582742 0.44748513
#> 10 0.40700559 0.41366376 0.41349216 0.41350336
#> 11 0.77762362 0.78373432 0.78262765 0.77949531
#> 12 0.64215450 0.63343656 0.63197252 0.62875264
#> 13 0.18750911 0.18653354 0.17822088 0.17842513
#> 14 0.26050129 0.27908122 0.27196942 0.26996735
#> 15 0.09594988 0.09089468 0.08865039 0.09080522
#> 16 0.00000000 0.00000000 0.00000000 0.00000000
#> 17 0.33619355 0.33546848 0.33501592 0.33670840
#> 18 0.58067856 0.58144968 0.58013547 0.57927640
#> 19 0.24670131 0.24099118 0.23822863 0.23157123
#> 20 0.38607475 0.40041929 0.40843493 0.40860630
#> 21 0.54933618 0.53959876 0.54139679 0.54098845
#> 22 0.26532303 0.26288937 0.27124503 0.27372049
#> 23 0.25426484 0.24411174 0.24044626 0.24169731
#> 24 0.13629899 0.14290857 0.14863967 0.15404679
#> 25 0.65036784 0.64494406 0.64644697 0.64416177
#> 26 0.19040219 0.19924562 0.20152378 0.20582432
#> 27 0.45428317 0.44621001 0.43311399 0.43568840
#> 28 0.11808149 0.11758965 0.11048814 0.11155803
#> 29 0.42401800 0.41725604 0.42362830 0.42160066
#> 30 0.16869228 0.17404171 0.17387211 0.17480429
#> 31 0.43243579 0.42525949 0.43329412 0.43856907
#> 32 0.11698190 0.10877882 0.10636129 0.10689920
#> 33 0.45073883 0.45206217 0.44881973 0.44992624
#> 34 0.09586296 0.09225400 0.09980718 0.10084230
#> 35 0.00000000 0.00000000 0.00000000 0.00000000
#> 36 0.43366789 0.44959439 0.44854107 0.44960414
#> 37 0.31162762 0.31502065 0.31958491 0.32067580
#> 38 0.16099729 0.15122241 0.15079738 0.14817778
#> 39 0.25364311 0.25507184 0.25875102 0.25674198
#> 40 0.53034707 0.52840262 0.52790558 0.52538704
#> 41 0.10186998 0.09060473 0.09445259 0.09150467
#> 42 0.48313687 0.48749107 0.48860245 0.49212211
#> 43 0.57309187 0.54660927 0.54001848 0.54366888
#> 44 0.00000000 0.00000000 0.00000000 0.00000000
#> 45 0.16498860 0.17052210 0.16781405 0.16715155
#> 46 0.64508459 0.65153585 0.65177845 0.65178893
#> 47 0.11991376 0.11705391 0.11873700 0.11591426
#> 48 0.16699846 0.16060597 0.16869180 0.16855238
#> 49 0.61300910 0.60887376 0.60785525 0.61021103
#> 50 0.31497806 0.31197484 0.30517646 0.31174053
#> 51 0.31832188 0.32079774 0.31281691 0.31540371
#> 52 0.00000000 0.00000000 0.00000000 0.00000000
#> 53 0.41513665 0.41604404 0.42408314 0.42075636
#> 54 0.47968839 0.47130262 0.46613648 0.46656474
#> 55 0.21377295 0.18935809 0.18930563 0.19050731
#> 56 0.57455838 0.57977566 0.57922184 0.57800858
#> 57 0.97541446 0.96885561 0.96385549 0.96028958
#> 58 0.63163263 0.64101334 0.63785489 0.64045306
#> 59 0.28914861 0.27879135 0.28784917 0.28782462
#> 60 0.55902384 0.56163014 0.55671889 0.55726966
#> 61 0.65941211 0.65058824 0.64930310 0.65091617
#> 62 0.60397744 0.58982660 0.58812271 0.58793743
#> 63 0.61771766 0.61445477 0.61166047 0.60985403
#> 64 1.00000000 1.00000000 1.00000000 1.00000000
#> 65 0.69769474 0.68981063 0.69214943 0.69038662
#> 66 0.53236916 0.53593471 0.53171176 0.53102146
#> 67 1.00000000 1.00000000 1.00000000 1.00000000
#> 68 0.81246241 0.79688369 0.80360414 0.80689017
#> 69 0.77466902 0.76435744 0.76435231 0.76730276
#> 70 0.92635688 0.91649616 0.91637035 0.92383169
#> 71 0.31958498 0.34299668 0.34334588 0.34349096
#> 72 0.06488309 0.05328264 0.04500807 0.04571864
#> 73 0.65602434 0.64784140 0.64971036 0.64930911
#> 74 0.94553110 0.94743335 0.95548576 0.95086945
#> 75 0.58105153 0.58651055 0.58350518 0.58195688
#> 76 0.50712414 0.50370011 0.50352891 0.50289031
#> 77 1.00000000 1.00000000 1.00000000 1.00000000
#> 78 0.91407469 0.92199353 0.92611779 0.92473772
#> 79 0.68969952 0.69332276 0.69306283 0.69031633
#> 80 0.29656001 0.28290751 0.28672758 0.28334638
#> 81 0.69260333 0.69271104 0.69448149 0.69353087
#> 82 0.75551561 0.75380626 0.76075515 0.76006115
#> 83 0.98111451 0.96789911 0.96789876 0.97167928
#> 84 0.92825575 0.93456912 0.93157477 0.93560641
#> 85 0.32670221 0.33419177 0.33780085 0.33863677
#> 86 0.80658855 0.82379298 0.82202715 0.81981455
#> 87 0.56043949 0.54972085 0.54609905 0.54656224
#> 88 0.58884182 0.58228453 0.57334028 0.57098492
#> 89 0.45579085 0.46221532 0.46149391 0.46635298
#> 90 1.00000000 1.00000000 1.00000000 1.00000000
#> 91 0.52873440 0.54203308 0.53789411 0.54025902
#> 92 0.94412285 0.93390914 0.93429933 0.92818920
#> 93 0.98499381 0.99987093 0.99916201 0.99970721
#> 94 0.61780332 0.60462097 0.60316670 0.59998131
#> 95 0.59883886 0.61566575 0.61062288 0.61003734
#> 96 0.39373512 0.38230743 0.38698306 0.38928062
#> 97 0.54534135 0.54894600 0.55196226 0.55110104
#> 98 0.84908812 0.83344560 0.82896512 0.82882885
#> 99 0.12518468 0.14252417 0.14250387 0.14467219
#> 100 0.74023431 0.74464225 0.75159300 0.75722807
#> 101 0.77862492 0.77776152 0.78102072 0.77766043
#> 102 0.75586058 0.76487586 0.76840548 0.77181270
#> 103 0.48885501 0.49542376 0.50188675 0.50526664
#> 104 0.80418072 0.82546481 0.81861016 0.81635531
modpls.aze$Probs-modpls.aze$Probs.trc
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 2 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 3 0 -0.09080494 -0.05104846 -0.171669164 -0.21455242 -0.21725391
#> 4 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 5 0 -0.04408124 0.00000000 -0.071299085 -0.24018962 -0.23445282
#> 6 0 -0.03776963 0.00000000 0.000000000 -0.02597539 -0.06284454
#> 7 0 -0.06930728 -0.19928456 -0.091372612 0.00000000 0.00000000
#> 8 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 9 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 10 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 11 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 12 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 13 0 0.00000000 -0.04344681 0.000000000 0.00000000 0.00000000
#> 14 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 15 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 16 0 0.00000000 -0.07276258 -0.051465557 -0.09988241 -0.06790398
#> 17 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 18 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 19 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 20 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 21 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 22 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 23 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 24 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 25 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 26 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 27 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 28 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 29 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 30 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 31 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 32 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 33 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 34 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 35 0 0.00000000 0.00000000 -0.088960742 -0.06232370 -0.08231459
#> 36 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 37 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 38 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 39 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 40 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 41 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 42 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 43 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 44 0 0.00000000 0.00000000 0.000000000 -0.01790809 -0.03785626
#> 45 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 46 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 47 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 48 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 49 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 50 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 51 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 52 0 -0.23250098 -0.28713647 -0.092161735 -0.12709475 -0.18324647
#> 53 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 54 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 55 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 56 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 57 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 58 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 59 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 60 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 61 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 62 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 63 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 64 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 65 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 66 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 67 0 0.00000000 0.17012215 0.081167947 0.22497425 0.21728258
#> 68 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 69 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 70 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 71 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 72 0 0.00000000 0.00000000 -0.044738287 -0.05529233 0.00000000
#> 73 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 74 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 75 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 76 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 77 0 0.00000000 0.00000000 0.000000000 0.10301473 0.08723742
#> 78 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 79 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 80 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 81 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 82 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 83 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 84 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 85 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 86 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 87 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 88 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 89 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 90 0 0.00000000 0.07649866 0.008644293 0.06363018 0.09017457
#> 91 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 92 0 0.00000000 0.03707888 0.000000000 0.00000000 0.00000000
#> 93 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 94 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 95 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 96 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 97 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 98 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 99 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 100 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 101 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 102 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 103 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> 104 0 0.00000000 0.00000000 0.000000000 0.00000000 0.00000000
#> [,7] [,8] [,9] [,10] [,11]
#> 1 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 2 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 3 -0.224440890 -0.193652144 -0.201652437 -0.20167289 -0.200815325
#> 4 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 5 -0.263270486 -0.311781941 -0.310765976 -0.31066830 -0.314013823
#> 6 -0.103410961 -0.076840858 -0.080016598 -0.08436785 -0.087771351
#> 7 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 8 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 9 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 10 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 11 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 12 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 13 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 14 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 15 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 16 -0.062392124 -0.039505632 -0.023469898 -0.02045198 -0.027151896
#> 17 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 18 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 19 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 20 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 21 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 22 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 23 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 24 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 25 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 26 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 27 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 28 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 29 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 30 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 31 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 32 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 33 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 34 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 35 -0.083905063 -0.085155814 -0.086690885 -0.08262604 -0.082725745
#> 36 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 37 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 38 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 39 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 40 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 41 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 42 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 43 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 44 -0.014514870 -0.004369241 -0.002647953 -0.00844687 -0.010738872
#> 45 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 46 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 47 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 48 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 49 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 50 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 51 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 52 -0.195842587 -0.184768370 -0.177691131 -0.18225253 -0.181720401
#> 53 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 54 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 55 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 56 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 57 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 58 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 59 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 60 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 61 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 62 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 63 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 64 0.001662791 0.008968255 0.008852814 0.01022555 0.006667285
#> 65 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 66 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 67 0.221111495 0.202569147 0.194030443 0.19310867 0.192281835
#> 68 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 69 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 70 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 71 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 72 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 73 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 74 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 75 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 76 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 77 0.064638063 0.090636226 0.099163425 0.09692704 0.096849873
#> 78 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 79 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 80 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 81 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 82 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 83 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 84 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 85 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 86 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 87 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 88 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 89 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 90 0.087035865 0.113909884 0.123382027 0.12669615 0.125596908
#> 91 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 92 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 93 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 94 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 95 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 96 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 97 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 98 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 99 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 100 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 101 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 102 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 103 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#> 104 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000
#Repeated cross validation of the model (NK=100 times)
cv.modpls.aze<-cv.plsR(y~.,data=aze_compl,10,NK=100, verbose=FALSE)
res.cv.modpls.aze<-cvtable(summary(cv.modpls.aze,MClassed=TRUE))
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
#> NK: 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
#> NK: 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
#> NK: 31, 32, 33, 34, 35, 36, 37, 38, 39, 40
#> NK: 41, 42, 43, 44, 45, 46, 47, 48, 49, 50
#> NK: 51, 52, 53, 54, 55, 56, 57, 58, 59, 60
#> NK: 61, 62, 63, 64, 65, 66, 67, 68, 69, 70
#> NK: 71, 72, 73, 74, 75, 76, 77, 78, 79, 80
#> NK: 81, 82, 83, 84, 85, 86, 87, 88, 89, 90
#> NK: 91, 92, 93, 94, 95, 96, 97, 98, 99, 100
#>
#> CV MissClassed criterion:
#> 1 2 3 4 5 6 7 8 9 10
#> 24 10 25 11 5 5 7 8 2 3
#>
#> CV Q2 criterion:
#> 0
#> 100
#>
#> CV Press criterion:
#> 1 2
#> 81 19
#High discrepancy in the number of component choice using repeated cross validation
#and missclassed criterion
plot(res.cv.modpls.aze)
rm(list=c("Xaze_compl","yaze_compl","modpls.aze","cv.modpls.aze","res.cv.modpls.aze"))
#24 predictors
dimX <- 24
#2 components
Astar <- 2
simul_data_UniYX(dimX,Astar)
#> Y X1 X2 X3 X4 X5 X6
#> 11.6445768 0.6175008 0.6056228 5.1301635 0.6107449 0.6138664 5.1302654
#> X7 X8 X9 X10 X11 X12 X13
#> 0.6402793 0.6103912 5.1466522 0.6048168 0.6205430 5.1257361 0.6101874
#> X14 X15 X16 X17 X18 X19 X20
#> 0.6103582 5.1152508 0.6286553 0.6139872 5.1205242 0.6145876 0.6165494
#> X21 X22 X23 X24
#> 5.1176202 0.6228756 0.6110989 5.1274519
dataAstar2 <- data.frame(t(replicate(250,simul_data_UniYX(dimX,Astar))))
modpls.A2<- plsR(Y~.,data=dataAstar2,10,typeVC="standard")
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
modpls.A2
#> Number of required components:
#> [1] 10
#> Number of successfully computed components:
#> [1] 10
#> Coefficients:
#> [,1]
#> Intercept -0.009308745
#> X1 -1.252039537
#> X2 0.251769360
#> X3 1.069303144
#> X4 -1.523206574
#> X5 0.429339251
#> X6 -0.103727035
#> X7 0.514838746
#> X8 -0.008132813
#> X9 -1.109769998
#> X10 0.697707208
#> X11 0.110179634
#> X12 0.263432702
#> X13 0.048981023
#> X14 1.102957195
#> X15 2.998639072
#> X16 -1.030175931
#> X17 0.005090035
#> X18 -0.108388491
#> X19 1.071595090
#> X20 0.619985470
#> X21 -0.996526239
#> X22 -0.639107098
#> X23 0.152756799
#> X24 0.266087715
#> Leave one out cross validated PRESS, Information criteria and Fit statistics:
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 1707.6611 NA NA NA NA 13336.080111
#> Nb_Comp_1 1268.8401 0.8246912 0.0975 0.82469120 2337.932230 2286.881298
#> Nb_Comp_2 -202.5201 0.9995127 0.0975 0.99722014 6.357207 6.306012
#> Nb_Comp_3 -219.4997 0.9994751 0.0975 -0.07700506 6.791607 5.844991
#> Nb_Comp_4 -219.7889 0.9993831 0.0975 -0.17536483 6.869997 5.791713
#> Nb_Comp_5 -218.0111 0.9992724 0.0975 -0.17939626 6.830725 5.786567
#> Nb_Comp_6 -216.0264 0.9991613 0.0975 -0.15268975 6.670117 5.786214
#> Nb_Comp_7 -214.0278 0.9990390 0.0975 -0.14581108 6.629908 5.786181
#> Nb_Comp_8 -212.0280 0.9989079 0.0975 -0.13650388 6.576018 5.786178
#> Nb_Comp_9 -210.0280 0.9987662 0.0975 -0.12969385 6.536609 5.786177
#> Nb_Comp_10 -208.0280 0.9986200 0.0975 -0.11849061 6.471785 5.786177
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 249.0000000 NA NA NA
#> Nb_Comp_1 0.8285192 0.8285192 42.6987119 43.6518917 0.82469120 0.0975
#> Nb_Comp_2 0.9995271 0.9995271 0.1177405 0.1186964 0.99722014 0.0975
#> Nb_Comp_3 0.9995617 0.9995617 0.1091327 0.1268071 -0.07700506 0.0975
#> Nb_Comp_4 0.9995657 0.9995657 0.1081380 0.1282708 -0.17536483 0.0975
#> Nb_Comp_5 0.9995661 0.9995661 0.1080419 0.1275375 -0.17939626 0.0975
#> Nb_Comp_6 0.9995661 0.9995661 0.1080353 0.1245388 -0.15268975 0.0975
#> Nb_Comp_7 0.9995661 0.9995661 0.1080347 0.1237880 -0.14581108 0.0975
#> Nb_Comp_8 0.9995661 0.9995661 0.1080346 0.1227818 -0.13650388 0.0975
#> Nb_Comp_9 0.9995661 0.9995661 0.1080346 0.1220460 -0.12969385 0.0975
#> Nb_Comp_10 0.9995661 0.9995661 0.1080346 0.1208357 -0.11849061 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof
#> Nb_Comp_0 NA 712.4673 1.000000 7.3183710 53.77278888
#> Nb_Comp_1 0.8246912 273.6462 2.555426 3.0339405 9.33570257
#> Nb_Comp_2 0.9995127 -1197.7140 3.000068 0.1594599 0.02583432
#> Nb_Comp_3 0.9994751 -1214.6936 22.575083 0.1599632 0.02800122
#> Nb_Comp_4 0.9993831 -1214.9828 24.169579 0.1597912 0.02810387
#> Nb_Comp_5 0.9992724 -1213.2050 24.064193 0.1596831 0.02805511
#> Nb_Comp_6 0.9991613 -1211.2203 23.898718 0.1596200 0.02801609
#> Nb_Comp_7 0.9990390 -1209.2217 23.943396 0.1596353 0.02802600
#> Nb_Comp_8 0.9989079 -1207.2219 23.912174 0.1596243 0.02801895
#> Nb_Comp_9 0.9987662 -1205.2219 24.027467 0.1596648 0.02804494
#> Nb_Comp_10 0.9986200 -1203.2219 24.166857 0.1597139 0.02807639
#> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive
#> Nb_Comp_0 54.52720631 499.7901 1 7.3183710 53.77278888
#> Nb_Comp_1 9.66703221 288.0890 2 3.0366586 9.29506592
#> Nb_Comp_2 0.02690885 -438.1210 3 0.1597824 0.02583678
#> Nb_Comp_3 0.03613800 -342.0265 4 0.1541432 0.02414029
#> Nb_Comp_4 0.03679663 -335.0836 5 0.1537519 0.02411244
#> Nb_Comp_5 0.03669827 -335.7098 6 0.1539982 0.02428461
#> Nb_Comp_6 0.03659303 -336.5430 7 0.1543100 0.02447830
#> Nb_Comp_7 0.03662062 -336.3205 8 0.1546281 0.02467496
#> Nb_Comp_8 0.03660118 -336.4765 9 0.1549485 0.02487336
#> Nb_Comp_9 0.03667292 -335.9008 10 0.1552710 0.02507344
#> Nb_Comp_10 0.03675976 -335.2051 11 0.1555955 0.02527518
#> BIC.naive GMDL.naive
#> Nb_Comp_0 54.52720631 499.7901
#> Nb_Comp_1 9.55484538 286.8472
#> Nb_Comp_2 0.02691563 -437.6224
#> Nb_Comp_3 0.02547901 -441.0025
#> Nb_Comp_4 0.02577736 -436.2568
#> Nb_Comp_5 0.02628892 -430.5960
#> Nb_Comp_6 0.02682615 -424.9195
#> Nb_Comp_7 0.02736928 -419.3100
#> Nb_Comp_8 0.02791705 -413.7646
#> Nb_Comp_9 0.02846940 -408.2768
#> Nb_Comp_10 0.02902638 -402.8412
cv.modpls.A2<-cv.plsR(Y~.,data=dataAstar2,10,NK=100, verbose=FALSE)
res.cv.modpls.A2<-cvtable(summary(cv.modpls.A2,verbose=FALSE))
#> Error in eval(mf, parent.frame()): object 'dataAstar2' not found
#Perfect choice for the Q2 criterion in PLSR
plot(res.cv.modpls.A2)
#> Error: object 'res.cv.modpls.A2' not found
#Binarized data.frame
simbin1 <- data.frame(dicho(dataAstar2))
modpls.B2 <- plsR(Y~.,data=simbin1,10,typeVC="standard",MClassed=TRUE, verbose=FALSE)
modpls.B2
#> Number of required components:
#> [1] 10
#> Number of successfully computed components:
#> [1] 8
#> Coefficients:
#> [,1]
#> Intercept -2.232996e-02
#> X1 -2.564600e-04
#> X2 1.827582e-02
#> X3 3.200076e-01
#> X4 1.827582e-02
#> X5 8.548667e-05
#> X6 -7.441941e-02
#> X7 1.827582e-02
#> X8 8.548667e-05
#> X9 1.600038e-01
#> X10 -6.653264e-03
#> X11 1.794648e-02
#> X12 5.540587e-01
#> X13 1.794648e-02
#> X14 -6.307629e-03
#> X15 -7.441941e-02
#> X16 1.827582e-02
#> X17 1.827582e-02
#> X18 -7.441941e-02
#> X19 -1.961416e-02
#> X20 -6.653264e-03
#> X21 -7.441941e-02
#> X22 8.548667e-05
#> X23 1.827582e-02
#> X24 1.600038e-01
#> Leave one out cross validated PRESS, Information criteria and Fit statistics:
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 366.87968 NA NA NA NA 62.496000
#> Nb_Comp_1 26.83779 0.7381855 0.0975 0.7381855034 16.362359 15.909796
#> Nb_Comp_2 -76.52049 0.8252118 0.0975 0.3323966613 10.621433 10.438510
#> Nb_Comp_3 -91.51975 0.8283713 0.0975 0.0180764406 10.249819 9.752317
#> Nb_Comp_4 -89.75294 0.8284216 0.0975 0.0002930630 9.749459 9.743224
#> Nb_Comp_5 -87.81866 0.8284417 0.0975 0.0001171978 9.742083 9.740663
#> Nb_Comp_6 -85.82121 0.8284216 0.0975 -0.0001170090 9.741803 9.740564
#> Nb_Comp_7 -83.82127 0.8283970 0.0975 -0.0001436374 9.741963 9.740562
#> Nb_Comp_8 -81.82127 0.8283740 0.0975 -0.0001343106 9.741870 9.740562
#> R2_Y MissClassed R2_residY RSS_residY PRESS_residY Q2_residY
#> Nb_Comp_0 NA 124 NA 249.00000 NA NA
#> Nb_Comp_1 0.7454270 11 0.7454270 63.38868 65.19181 0.7381855034
#> Nb_Comp_2 0.8329731 13 0.8329731 41.58969 42.31850 0.3323966613
#> Nb_Comp_3 0.8439529 11 0.8439529 38.85572 40.83789 0.0180764406
#> Nb_Comp_4 0.8440984 11 0.8440984 38.81949 38.84433 0.0002930630
#> Nb_Comp_5 0.8441394 11 0.8441394 38.80929 38.81494 0.0001171978
#> Nb_Comp_6 0.8441410 11 0.8441410 38.80889 38.81383 -0.0001170090
#> Nb_Comp_7 0.8441410 11 0.8441410 38.80888 38.81447 -0.0001436374
#> Nb_Comp_8 0.8441410 11 0.8441410 38.80888 38.81409 -0.0001343106
#> LimQ2 Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof
#> Nb_Comp_0 NA NA 712.4673 1.000000 0.5009870 0.25199190
#> Nb_Comp_1 0.0975 0.7381855 372.4254 3.112471 0.2533407 0.06523729
#> Nb_Comp_2 0.0975 0.8252118 269.0671 3.041696 0.2051776 0.04277843
#> Nb_Comp_3 0.0975 0.8283713 254.0678 10.053091 0.2011839 0.04226445
#> Nb_Comp_4 0.0975 0.8284216 255.8346 8.294626 0.2003603 0.04163675
#> Nb_Comp_5 0.0975 0.8284417 257.7689 8.533342 0.2004326 0.04170515
#> Nb_Comp_6 0.0975 0.8284216 259.7664 8.818006 0.2005493 0.04179954
#> Nb_Comp_7 0.0975 0.8283970 261.7663 8.983410 0.2006178 0.04185473
#> Nb_Comp_8 0.0975 0.8283740 263.7663 8.999382 0.2006244 0.04186006
#> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive
#> Nb_Comp_0 0.25552728 -167.2823 1 0.5009870 0.25199190 0.25552728
#> Nb_Comp_1 0.06805112 -330.7000 2 0.2532832 0.06466562 0.06647290
#> Nb_Comp_2 0.04458211 -382.8861 3 0.2055752 0.04276831 0.04455416
#> Nb_Comp_3 0.04799596 -369.7858 4 0.1991069 0.04027786 0.04251151
#> Nb_Comp_4 0.04632708 -374.9363 5 0.1994198 0.04056363 0.04336448
#> Nb_Comp_5 0.04653394 -374.2734 6 0.1998018 0.04087885 0.04425275
#> Nb_Comp_6 0.04679523 -373.4508 7 0.2002115 0.04120700 0.04515938
#> Nb_Comp_7 0.04694760 -372.9744 8 0.2006247 0.04153826 0.04607393
#> Nb_Comp_8 0.04696232 -372.9284 9 0.2010405 0.04187229 0.04699609
#> GMDL.naive
#> Nb_Comp_0 -167.2823
#> Nb_Comp_1 -333.8147
#> Nb_Comp_2 -382.5286
#> Nb_Comp_3 -387.5578
#> Nb_Comp_4 -384.4408
#> Nb_Comp_5 -381.3439
#> Nb_Comp_6 -378.3019
#> Nb_Comp_7 -375.3325
#> Nb_Comp_8 -372.4277
modpls.B2$Probs
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 2 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 3 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 4 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 5 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 6 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 7 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 8 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 9 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 10 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 11 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 12 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 13 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 14 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 15 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 16 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 17 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 18 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 19 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 20 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 21 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 22 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 23 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 24 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 25 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 26 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 27 0.496 0.03606045 0.002955833 0.0001432602 -0.01599899 0.007925162
#> 28 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 29 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 30 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 31 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 32 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 33 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 34 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 35 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 36 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 37 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 38 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 39 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 40 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 41 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 42 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 43 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 44 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 45 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 46 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 47 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 48 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 49 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 50 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 51 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 52 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 53 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 54 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 55 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 56 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 57 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 58 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 59 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 60 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 61 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 62 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 63 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 64 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 65 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 66 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 67 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 68 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 69 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 70 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 71 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 72 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 73 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 74 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 75 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 76 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 77 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 78 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 79 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 80 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 81 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 82 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 83 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 84 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 85 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 86 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 87 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 88 0.496 1.00500492 0.973254333 1.0029362526 0.99021061 0.997293320
#> 89 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 90 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 91 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 92 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 93 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 94 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 95 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 96 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 97 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 98 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 99 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 100 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 101 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 102 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 103 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 104 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 105 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 106 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 107 0.496 0.90947840 0.945577585 1.0369797504 0.97619695 0.995322751
#> 108 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 109 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 110 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 111 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 112 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 113 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 114 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 115 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 116 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 117 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 118 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 119 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 120 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 121 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 122 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 123 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 124 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 125 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 126 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 127 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 128 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 129 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 130 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 131 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 132 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 133 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 134 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 135 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 136 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 137 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 138 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 139 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 140 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 141 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 142 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 143 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 144 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 145 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 146 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 147 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 148 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 149 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 150 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 151 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 152 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 153 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 154 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 155 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 156 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 157 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 158 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 159 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 160 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 161 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 162 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 163 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 164 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 165 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 166 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 167 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 168 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 169 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 170 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 171 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 172 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 173 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 174 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 175 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 176 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 177 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 178 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 179 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 180 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 181 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 182 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 183 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 184 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 185 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 186 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 187 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 188 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 189 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 190 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 191 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 192 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 193 0.496 0.45907245 0.636656724 0.0755600483 0.02597481 0.001514519
#> 194 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 195 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 196 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 197 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 198 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 199 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 200 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 201 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 202 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 203 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 204 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 205 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 206 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 207 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 208 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 209 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 210 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 211 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 212 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 213 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 214 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 215 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 216 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 217 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 218 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 219 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 220 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 221 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 222 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 223 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 224 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 225 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 226 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 227 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 228 0.496 0.95759591 0.959727077 0.9862193637 1.01721602 0.999177807
#> 229 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 230 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 231 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 232 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 233 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 234 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 235 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 236 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 237 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 238 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 239 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 240 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 241 0.496 0.36839434 0.524751547 -0.0668244029 -0.02771783 -0.002376501
#> 242 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 243 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 244 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> 245 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 246 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 247 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 248 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 249 0.496 1.02826346 0.979507152 0.9800896768 0.98049332 0.980469640
#> 250 0.496 -0.08206690 -0.030322806 -0.0223110698 -0.02215549 -0.022410075
#> [,7] [,8] [,9]
#> 1 8.740665e-01 8.740690e-01 8.740663e-01
#> 2 9.803926e-01 9.803899e-01 9.803858e-01
#> 3 9.803926e-01 9.803899e-01 9.803858e-01
#> 4 8.740665e-01 8.740690e-01 8.740663e-01
#> 5 9.803926e-01 9.803899e-01 9.803858e-01
#> 6 9.803926e-01 9.803899e-01 9.803858e-01
#> 7 8.740665e-01 8.740690e-01 8.740663e-01
#> 8 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 9 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 10 9.803926e-01 9.803899e-01 9.803858e-01
#> 11 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 12 9.803926e-01 9.803899e-01 9.803858e-01
#> 13 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 14 8.740665e-01 8.740690e-01 8.740663e-01
#> 15 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 16 8.399153e-02 8.399119e-02 8.398958e-02
#> 17 9.803926e-01 9.803899e-01 9.803858e-01
#> 18 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 19 8.399153e-02 8.399119e-02 8.398958e-02
#> 20 8.399153e-02 8.399119e-02 8.398958e-02
#> 21 9.803926e-01 9.803899e-01 9.803858e-01
#> 22 8.399153e-02 8.399119e-02 8.398958e-02
#> 23 8.399153e-02 8.399119e-02 8.398958e-02
#> 24 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 25 9.803926e-01 9.803899e-01 9.803858e-01
#> 26 9.803926e-01 9.803899e-01 9.803858e-01
#> 27 1.814101e-04 -9.424424e-05 1.665335e-16
#> 28 8.740665e-01 8.740690e-01 8.740663e-01
#> 29 9.803926e-01 9.803899e-01 9.803858e-01
#> 30 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 31 9.803926e-01 9.803899e-01 9.803858e-01
#> 32 9.803926e-01 9.803899e-01 9.803858e-01
#> 33 8.740665e-01 8.740690e-01 8.740663e-01
#> 34 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 35 8.740665e-01 8.740690e-01 8.740663e-01
#> 36 9.803926e-01 9.803899e-01 9.803858e-01
#> 37 9.803926e-01 9.803899e-01 9.803858e-01
#> 38 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 39 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 40 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 41 8.740665e-01 8.740690e-01 8.740663e-01
#> 42 8.399153e-02 8.399119e-02 8.398958e-02
#> 43 8.740665e-01 8.740690e-01 8.740663e-01
#> 44 8.399153e-02 8.399119e-02 8.398958e-02
#> 45 8.740665e-01 8.740690e-01 8.740663e-01
#> 46 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 47 9.803926e-01 9.803899e-01 9.803858e-01
#> 48 8.740665e-01 8.740690e-01 8.740663e-01
#> 49 8.740665e-01 8.740690e-01 8.740663e-01
#> 50 8.399153e-02 8.399119e-02 8.398958e-02
#> 51 8.399153e-02 8.399119e-02 8.398958e-02
#> 52 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 53 9.803926e-01 9.803899e-01 9.803858e-01
#> 54 8.740665e-01 8.740690e-01 8.740663e-01
#> 55 9.803926e-01 9.803899e-01 9.803858e-01
#> 56 9.803926e-01 9.803899e-01 9.803858e-01
#> 57 9.803926e-01 9.803899e-01 9.803858e-01
#> 58 8.399153e-02 8.399119e-02 8.398958e-02
#> 59 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 60 9.803926e-01 9.803899e-01 9.803858e-01
#> 61 8.740665e-01 8.740690e-01 8.740663e-01
#> 62 8.740665e-01 8.740690e-01 8.740663e-01
#> 63 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 64 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 65 9.803926e-01 9.803899e-01 9.803858e-01
#> 66 9.803926e-01 9.803899e-01 9.803858e-01
#> 67 9.803926e-01 9.803899e-01 9.803858e-01
#> 68 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 69 8.399153e-02 8.399119e-02 8.398958e-02
#> 70 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 71 8.399153e-02 8.399119e-02 8.398958e-02
#> 72 8.399153e-02 8.399119e-02 8.398958e-02
#> 73 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 74 8.399153e-02 8.399119e-02 8.398958e-02
#> 75 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 76 8.740665e-01 8.740690e-01 8.740663e-01
#> 77 8.399153e-02 8.399119e-02 8.398958e-02
#> 78 8.399153e-02 8.399119e-02 8.398958e-02
#> 79 9.803926e-01 9.803899e-01 9.803858e-01
#> 80 8.740665e-01 8.740690e-01 8.740663e-01
#> 81 8.740665e-01 8.740690e-01 8.740663e-01
#> 82 8.740665e-01 8.740690e-01 8.740663e-01
#> 83 8.399153e-02 8.399119e-02 8.398958e-02
#> 84 8.740665e-01 8.740690e-01 8.740663e-01
#> 85 8.399153e-02 8.399119e-02 8.398958e-02
#> 86 8.399153e-02 8.399119e-02 8.398958e-02
#> 87 9.803926e-01 9.803899e-01 9.803858e-01
#> 88 9.986012e-01 9.998504e-01 1.000000e+00
#> 89 9.803926e-01 9.803899e-01 9.803858e-01
#> 90 8.740665e-01 8.740690e-01 8.740663e-01
#> 91 8.399153e-02 8.399119e-02 8.398958e-02
#> 92 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 93 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 94 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 95 9.803926e-01 9.803899e-01 9.803858e-01
#> 96 8.399153e-02 8.399119e-02 8.398958e-02
#> 97 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 98 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 99 9.803926e-01 9.803899e-01 9.803858e-01
#> 100 8.740665e-01 8.740690e-01 8.740663e-01
#> 101 9.803926e-01 9.803899e-01 9.803858e-01
#> 102 8.399153e-02 8.399119e-02 8.398958e-02
#> 103 9.803926e-01 9.803899e-01 9.803858e-01
#> 104 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 105 9.803926e-01 9.803899e-01 9.803858e-01
#> 106 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 107 1.000509e+00 9.999525e-01 1.000000e+00
#> 108 8.740665e-01 8.740690e-01 8.740663e-01
#> 109 8.399153e-02 8.399119e-02 8.398958e-02
#> 110 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 111 9.803926e-01 9.803899e-01 9.803858e-01
#> 112 9.803926e-01 9.803899e-01 9.803858e-01
#> 113 8.740665e-01 8.740690e-01 8.740663e-01
#> 114 8.399153e-02 8.399119e-02 8.398958e-02
#> 115 9.803926e-01 9.803899e-01 9.803858e-01
#> 116 8.740665e-01 8.740690e-01 8.740663e-01
#> 117 8.740665e-01 8.740690e-01 8.740663e-01
#> 118 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 119 8.399153e-02 8.399119e-02 8.398958e-02
#> 120 9.803926e-01 9.803899e-01 9.803858e-01
#> 121 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 122 8.740665e-01 8.740690e-01 8.740663e-01
#> 123 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 124 8.399153e-02 8.399119e-02 8.398958e-02
#> 125 8.399153e-02 8.399119e-02 8.398958e-02
#> 126 9.803926e-01 9.803899e-01 9.803858e-01
#> 127 9.803926e-01 9.803899e-01 9.803858e-01
#> 128 9.803926e-01 9.803899e-01 9.803858e-01
#> 129 9.803926e-01 9.803899e-01 9.803858e-01
#> 130 8.399153e-02 8.399119e-02 8.398958e-02
#> 131 9.803926e-01 9.803899e-01 9.803858e-01
#> 132 8.399153e-02 8.399119e-02 8.398958e-02
#> 133 9.803926e-01 9.803899e-01 9.803858e-01
#> 134 8.740665e-01 8.740690e-01 8.740663e-01
#> 135 9.803926e-01 9.803899e-01 9.803858e-01
#> 136 8.740665e-01 8.740690e-01 8.740663e-01
#> 137 9.803926e-01 9.803899e-01 9.803858e-01
#> 138 8.740665e-01 8.740690e-01 8.740663e-01
#> 139 8.399153e-02 8.399119e-02 8.398958e-02
#> 140 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 141 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 142 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 143 8.740665e-01 8.740690e-01 8.740663e-01
#> 144 8.740665e-01 8.740690e-01 8.740663e-01
#> 145 8.740665e-01 8.740690e-01 8.740663e-01
#> 146 8.399153e-02 8.399119e-02 8.398958e-02
#> 147 8.740665e-01 8.740690e-01 8.740663e-01
#> 148 9.803926e-01 9.803899e-01 9.803858e-01
#> 149 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 150 8.740665e-01 8.740690e-01 8.740663e-01
#> 151 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 152 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 153 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 154 9.803926e-01 9.803899e-01 9.803858e-01
#> 155 9.803926e-01 9.803899e-01 9.803858e-01
#> 156 9.803926e-01 9.803899e-01 9.803858e-01
#> 157 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 158 8.399153e-02 8.399119e-02 8.398958e-02
#> 159 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 160 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 161 8.399153e-02 8.399119e-02 8.398958e-02
#> 162 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 163 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 164 9.803926e-01 9.803899e-01 9.803858e-01
#> 165 8.399153e-02 8.399119e-02 8.398958e-02
#> 166 8.740665e-01 8.740690e-01 8.740663e-01
#> 167 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 168 8.740665e-01 8.740690e-01 8.740663e-01
#> 169 8.399153e-02 8.399119e-02 8.398958e-02
#> 170 9.803926e-01 9.803899e-01 9.803858e-01
#> 171 8.399153e-02 8.399119e-02 8.398958e-02
#> 172 8.740665e-01 8.740690e-01 8.740663e-01
#> 173 8.399153e-02 8.399119e-02 8.398958e-02
#> 174 9.803926e-01 9.803899e-01 9.803858e-01
#> 175 9.803926e-01 9.803899e-01 9.803858e-01
#> 176 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 177 9.803926e-01 9.803899e-01 9.803858e-01
#> 178 8.740665e-01 8.740690e-01 8.740663e-01
#> 179 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 180 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 181 8.399153e-02 8.399119e-02 8.398958e-02
#> 182 9.803926e-01 9.803899e-01 9.803858e-01
#> 183 8.399153e-02 8.399119e-02 8.398958e-02
#> 184 9.803926e-01 9.803899e-01 9.803858e-01
#> 185 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 186 9.803926e-01 9.803899e-01 9.803858e-01
#> 187 8.740665e-01 8.740690e-01 8.740663e-01
#> 188 8.740665e-01 8.740690e-01 8.740663e-01
#> 189 9.803926e-01 9.803899e-01 9.803858e-01
#> 190 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 191 9.803926e-01 9.803899e-01 9.803858e-01
#> 192 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 193 -4.099320e-05 -1.534115e-05 -1.110223e-16
#> 194 8.740665e-01 8.740690e-01 8.740663e-01
#> 195 8.399153e-02 8.399119e-02 8.398958e-02
#> 196 9.803926e-01 9.803899e-01 9.803858e-01
#> 197 8.740665e-01 8.740690e-01 8.740663e-01
#> 198 9.803926e-01 9.803899e-01 9.803858e-01
#> 199 8.740665e-01 8.740690e-01 8.740663e-01
#> 200 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 201 8.740665e-01 8.740690e-01 8.740663e-01
#> 202 8.399153e-02 8.399119e-02 8.398958e-02
#> 203 8.740665e-01 8.740690e-01 8.740663e-01
#> 204 8.740665e-01 8.740690e-01 8.740663e-01
#> 205 8.399153e-02 8.399119e-02 8.398958e-02
#> 206 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 207 9.803926e-01 9.803899e-01 9.803858e-01
#> 208 8.399153e-02 8.399119e-02 8.398958e-02
#> 209 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 210 9.803926e-01 9.803899e-01 9.803858e-01
#> 211 8.399153e-02 8.399119e-02 8.398958e-02
#> 212 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 213 9.803926e-01 9.803899e-01 9.803858e-01
#> 214 9.803926e-01 9.803899e-01 9.803858e-01
#> 215 9.803926e-01 9.803899e-01 9.803858e-01
#> 216 8.399153e-02 8.399119e-02 8.398958e-02
#> 217 8.399153e-02 8.399119e-02 8.398958e-02
#> 218 9.803926e-01 9.803899e-01 9.803858e-01
#> 219 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 220 8.399153e-02 8.399119e-02 8.398958e-02
#> 221 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 222 8.399153e-02 8.399119e-02 8.398958e-02
#> 223 8.740665e-01 8.740690e-01 8.740663e-01
#> 224 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 225 8.740665e-01 8.740690e-01 8.740663e-01
#> 226 8.740665e-01 8.740690e-01 8.740663e-01
#> 227 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 228 1.000372e+00 9.997744e-01 1.000000e+00
#> 229 9.803926e-01 9.803899e-01 9.803858e-01
#> 230 9.803926e-01 9.803899e-01 9.803858e-01
#> 231 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 232 9.803926e-01 9.803899e-01 9.803858e-01
#> 233 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 234 9.803926e-01 9.803899e-01 9.803858e-01
#> 235 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 236 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 237 8.399153e-02 8.399119e-02 8.398958e-02
#> 238 9.803926e-01 9.803899e-01 9.803858e-01
#> 239 9.803926e-01 9.803899e-01 9.803858e-01
#> 240 8.740665e-01 8.740690e-01 8.740663e-01
#> 241 5.931236e-05 -7.182277e-06 -2.775558e-16
#> 242 8.740665e-01 8.740690e-01 8.740663e-01
#> 243 9.803926e-01 9.803899e-01 9.803858e-01
#> 244 -2.233453e-02 -2.232974e-02 -2.232996e-02
#> 245 8.399153e-02 8.399119e-02 8.398958e-02
#> 246 8.399153e-02 8.399119e-02 8.398958e-02
#> 247 8.399153e-02 8.399119e-02 8.398958e-02
#> 248 8.399153e-02 8.399119e-02 8.398958e-02
#> 249 9.803926e-01 9.803899e-01 9.803858e-01
#> 250 -2.233453e-02 -2.232974e-02 -2.232996e-02
modpls.B2$Probs.trc
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 2 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 3 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 4 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 5 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 6 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 7 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 8 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 9 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 10 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 11 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 12 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 13 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 14 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 15 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 16 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 17 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 18 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 19 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 20 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 21 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 22 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 23 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 24 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 25 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 26 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 27 0.496 0.03606045 0.002955833 0.0001432602 0.00000000 0.007925162
#> 28 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 29 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 30 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 31 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 32 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 33 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 34 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 35 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 36 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 37 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 38 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 39 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 40 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 41 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 42 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 43 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 44 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 45 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 46 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 47 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 48 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 49 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 50 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 51 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 52 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 53 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 54 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 55 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 56 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 57 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 58 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 59 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 60 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 61 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 62 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 63 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 64 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 65 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 66 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 67 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 68 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 69 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 70 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 71 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 72 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 73 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 74 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 75 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 76 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 77 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 78 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 79 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 80 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 81 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 82 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 83 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 84 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 85 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 86 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 87 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 88 0.496 1.00000000 0.973254333 1.0000000000 0.99021061 0.997293320
#> 89 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 90 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 91 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 92 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 93 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 94 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 95 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 96 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 97 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 98 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 99 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 100 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 101 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 102 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 103 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 104 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 105 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 106 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 107 0.496 0.90947840 0.945577585 1.0000000000 0.97619695 0.995322751
#> 108 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 109 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 110 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 111 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 112 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 113 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 114 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 115 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 116 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 117 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 118 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 119 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 120 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 121 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 122 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 123 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 124 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 125 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 126 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 127 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 128 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 129 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 130 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 131 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 132 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 133 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 134 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 135 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 136 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 137 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 138 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 139 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 140 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 141 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 142 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 143 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 144 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 145 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 146 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 147 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 148 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 149 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 150 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 151 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 152 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 153 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 154 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 155 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 156 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 157 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 158 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 159 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 160 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 161 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 162 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 163 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 164 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 165 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 166 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 167 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 168 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 169 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 170 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 171 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 172 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 173 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 174 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 175 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 176 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 177 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 178 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 179 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 180 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 181 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 182 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 183 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 184 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 185 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 186 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 187 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 188 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 189 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 190 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 191 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 192 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 193 0.496 0.45907245 0.636656724 0.0755600483 0.02597481 0.001514519
#> 194 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 195 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 196 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 197 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 198 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 199 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 200 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 201 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 202 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 203 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 204 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 205 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 206 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 207 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 208 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 209 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 210 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 211 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 212 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 213 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 214 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 215 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 216 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 217 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 218 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 219 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 220 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 221 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 222 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 223 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 224 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 225 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 226 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 227 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 228 0.496 0.95759591 0.959727077 0.9862193637 1.00000000 0.999177807
#> 229 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 230 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 231 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 232 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 233 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 234 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 235 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 236 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 237 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 238 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 239 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 240 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 241 0.496 0.36839434 0.524751547 0.0000000000 0.00000000 0.000000000
#> 242 0.496 0.64117170 0.862240206 0.8736969788 0.87418616 0.874112455
#> 243 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 244 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> 245 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 246 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 247 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 248 0.496 0.30502486 0.086944139 0.0840816281 0.08415168 0.083947110
#> 249 0.496 1.00000000 0.979507152 0.9800896768 0.98049332 0.980469640
#> 250 0.496 0.00000000 0.000000000 0.0000000000 0.00000000 0.000000000
#> [,7] [,8] [,9]
#> 1 8.740665e-01 0.87406897 8.740663e-01
#> 2 9.803926e-01 0.98038990 9.803858e-01
#> 3 9.803926e-01 0.98038990 9.803858e-01
#> 4 8.740665e-01 0.87406897 8.740663e-01
#> 5 9.803926e-01 0.98038990 9.803858e-01
#> 6 9.803926e-01 0.98038990 9.803858e-01
#> 7 8.740665e-01 0.87406897 8.740663e-01
#> 8 0.000000e+00 0.00000000 0.000000e+00
#> 9 0.000000e+00 0.00000000 0.000000e+00
#> 10 9.803926e-01 0.98038990 9.803858e-01
#> 11 0.000000e+00 0.00000000 0.000000e+00
#> 12 9.803926e-01 0.98038990 9.803858e-01
#> 13 0.000000e+00 0.00000000 0.000000e+00
#> 14 8.740665e-01 0.87406897 8.740663e-01
#> 15 0.000000e+00 0.00000000 0.000000e+00
#> 16 8.399153e-02 0.08399119 8.398958e-02
#> 17 9.803926e-01 0.98038990 9.803858e-01
#> 18 0.000000e+00 0.00000000 0.000000e+00
#> 19 8.399153e-02 0.08399119 8.398958e-02
#> 20 8.399153e-02 0.08399119 8.398958e-02
#> 21 9.803926e-01 0.98038990 9.803858e-01
#> 22 8.399153e-02 0.08399119 8.398958e-02
#> 23 8.399153e-02 0.08399119 8.398958e-02
#> 24 0.000000e+00 0.00000000 0.000000e+00
#> 25 9.803926e-01 0.98038990 9.803858e-01
#> 26 9.803926e-01 0.98038990 9.803858e-01
#> 27 1.814101e-04 0.00000000 1.665335e-16
#> 28 8.740665e-01 0.87406897 8.740663e-01
#> 29 9.803926e-01 0.98038990 9.803858e-01
#> 30 0.000000e+00 0.00000000 0.000000e+00
#> 31 9.803926e-01 0.98038990 9.803858e-01
#> 32 9.803926e-01 0.98038990 9.803858e-01
#> 33 8.740665e-01 0.87406897 8.740663e-01
#> 34 0.000000e+00 0.00000000 0.000000e+00
#> 35 8.740665e-01 0.87406897 8.740663e-01
#> 36 9.803926e-01 0.98038990 9.803858e-01
#> 37 9.803926e-01 0.98038990 9.803858e-01
#> 38 0.000000e+00 0.00000000 0.000000e+00
#> 39 0.000000e+00 0.00000000 0.000000e+00
#> 40 0.000000e+00 0.00000000 0.000000e+00
#> 41 8.740665e-01 0.87406897 8.740663e-01
#> 42 8.399153e-02 0.08399119 8.398958e-02
#> 43 8.740665e-01 0.87406897 8.740663e-01
#> 44 8.399153e-02 0.08399119 8.398958e-02
#> 45 8.740665e-01 0.87406897 8.740663e-01
#> 46 0.000000e+00 0.00000000 0.000000e+00
#> 47 9.803926e-01 0.98038990 9.803858e-01
#> 48 8.740665e-01 0.87406897 8.740663e-01
#> 49 8.740665e-01 0.87406897 8.740663e-01
#> 50 8.399153e-02 0.08399119 8.398958e-02
#> 51 8.399153e-02 0.08399119 8.398958e-02
#> 52 0.000000e+00 0.00000000 0.000000e+00
#> 53 9.803926e-01 0.98038990 9.803858e-01
#> 54 8.740665e-01 0.87406897 8.740663e-01
#> 55 9.803926e-01 0.98038990 9.803858e-01
#> 56 9.803926e-01 0.98038990 9.803858e-01
#> 57 9.803926e-01 0.98038990 9.803858e-01
#> 58 8.399153e-02 0.08399119 8.398958e-02
#> 59 0.000000e+00 0.00000000 0.000000e+00
#> 60 9.803926e-01 0.98038990 9.803858e-01
#> 61 8.740665e-01 0.87406897 8.740663e-01
#> 62 8.740665e-01 0.87406897 8.740663e-01
#> 63 0.000000e+00 0.00000000 0.000000e+00
#> 64 0.000000e+00 0.00000000 0.000000e+00
#> 65 9.803926e-01 0.98038990 9.803858e-01
#> 66 9.803926e-01 0.98038990 9.803858e-01
#> 67 9.803926e-01 0.98038990 9.803858e-01
#> 68 0.000000e+00 0.00000000 0.000000e+00
#> 69 8.399153e-02 0.08399119 8.398958e-02
#> 70 0.000000e+00 0.00000000 0.000000e+00
#> 71 8.399153e-02 0.08399119 8.398958e-02
#> 72 8.399153e-02 0.08399119 8.398958e-02
#> 73 0.000000e+00 0.00000000 0.000000e+00
#> 74 8.399153e-02 0.08399119 8.398958e-02
#> 75 0.000000e+00 0.00000000 0.000000e+00
#> 76 8.740665e-01 0.87406897 8.740663e-01
#> 77 8.399153e-02 0.08399119 8.398958e-02
#> 78 8.399153e-02 0.08399119 8.398958e-02
#> 79 9.803926e-01 0.98038990 9.803858e-01
#> 80 8.740665e-01 0.87406897 8.740663e-01
#> 81 8.740665e-01 0.87406897 8.740663e-01
#> 82 8.740665e-01 0.87406897 8.740663e-01
#> 83 8.399153e-02 0.08399119 8.398958e-02
#> 84 8.740665e-01 0.87406897 8.740663e-01
#> 85 8.399153e-02 0.08399119 8.398958e-02
#> 86 8.399153e-02 0.08399119 8.398958e-02
#> 87 9.803926e-01 0.98038990 9.803858e-01
#> 88 9.986012e-01 0.99985040 1.000000e+00
#> 89 9.803926e-01 0.98038990 9.803858e-01
#> 90 8.740665e-01 0.87406897 8.740663e-01
#> 91 8.399153e-02 0.08399119 8.398958e-02
#> 92 0.000000e+00 0.00000000 0.000000e+00
#> 93 0.000000e+00 0.00000000 0.000000e+00
#> 94 0.000000e+00 0.00000000 0.000000e+00
#> 95 9.803926e-01 0.98038990 9.803858e-01
#> 96 8.399153e-02 0.08399119 8.398958e-02
#> 97 0.000000e+00 0.00000000 0.000000e+00
#> 98 0.000000e+00 0.00000000 0.000000e+00
#> 99 9.803926e-01 0.98038990 9.803858e-01
#> 100 8.740665e-01 0.87406897 8.740663e-01
#> 101 9.803926e-01 0.98038990 9.803858e-01
#> 102 8.399153e-02 0.08399119 8.398958e-02
#> 103 9.803926e-01 0.98038990 9.803858e-01
#> 104 0.000000e+00 0.00000000 0.000000e+00
#> 105 9.803926e-01 0.98038990 9.803858e-01
#> 106 0.000000e+00 0.00000000 0.000000e+00
#> 107 1.000000e+00 0.99995246 1.000000e+00
#> 108 8.740665e-01 0.87406897 8.740663e-01
#> 109 8.399153e-02 0.08399119 8.398958e-02
#> 110 0.000000e+00 0.00000000 0.000000e+00
#> 111 9.803926e-01 0.98038990 9.803858e-01
#> 112 9.803926e-01 0.98038990 9.803858e-01
#> 113 8.740665e-01 0.87406897 8.740663e-01
#> 114 8.399153e-02 0.08399119 8.398958e-02
#> 115 9.803926e-01 0.98038990 9.803858e-01
#> 116 8.740665e-01 0.87406897 8.740663e-01
#> 117 8.740665e-01 0.87406897 8.740663e-01
#> 118 0.000000e+00 0.00000000 0.000000e+00
#> 119 8.399153e-02 0.08399119 8.398958e-02
#> 120 9.803926e-01 0.98038990 9.803858e-01
#> 121 0.000000e+00 0.00000000 0.000000e+00
#> 122 8.740665e-01 0.87406897 8.740663e-01
#> 123 0.000000e+00 0.00000000 0.000000e+00
#> 124 8.399153e-02 0.08399119 8.398958e-02
#> 125 8.399153e-02 0.08399119 8.398958e-02
#> 126 9.803926e-01 0.98038990 9.803858e-01
#> 127 9.803926e-01 0.98038990 9.803858e-01
#> 128 9.803926e-01 0.98038990 9.803858e-01
#> 129 9.803926e-01 0.98038990 9.803858e-01
#> 130 8.399153e-02 0.08399119 8.398958e-02
#> 131 9.803926e-01 0.98038990 9.803858e-01
#> 132 8.399153e-02 0.08399119 8.398958e-02
#> 133 9.803926e-01 0.98038990 9.803858e-01
#> 134 8.740665e-01 0.87406897 8.740663e-01
#> 135 9.803926e-01 0.98038990 9.803858e-01
#> 136 8.740665e-01 0.87406897 8.740663e-01
#> 137 9.803926e-01 0.98038990 9.803858e-01
#> 138 8.740665e-01 0.87406897 8.740663e-01
#> 139 8.399153e-02 0.08399119 8.398958e-02
#> 140 0.000000e+00 0.00000000 0.000000e+00
#> 141 0.000000e+00 0.00000000 0.000000e+00
#> 142 0.000000e+00 0.00000000 0.000000e+00
#> 143 8.740665e-01 0.87406897 8.740663e-01
#> 144 8.740665e-01 0.87406897 8.740663e-01
#> 145 8.740665e-01 0.87406897 8.740663e-01
#> 146 8.399153e-02 0.08399119 8.398958e-02
#> 147 8.740665e-01 0.87406897 8.740663e-01
#> 148 9.803926e-01 0.98038990 9.803858e-01
#> 149 0.000000e+00 0.00000000 0.000000e+00
#> 150 8.740665e-01 0.87406897 8.740663e-01
#> 151 0.000000e+00 0.00000000 0.000000e+00
#> 152 0.000000e+00 0.00000000 0.000000e+00
#> 153 0.000000e+00 0.00000000 0.000000e+00
#> 154 9.803926e-01 0.98038990 9.803858e-01
#> 155 9.803926e-01 0.98038990 9.803858e-01
#> 156 9.803926e-01 0.98038990 9.803858e-01
#> 157 0.000000e+00 0.00000000 0.000000e+00
#> 158 8.399153e-02 0.08399119 8.398958e-02
#> 159 0.000000e+00 0.00000000 0.000000e+00
#> 160 0.000000e+00 0.00000000 0.000000e+00
#> 161 8.399153e-02 0.08399119 8.398958e-02
#> 162 0.000000e+00 0.00000000 0.000000e+00
#> 163 0.000000e+00 0.00000000 0.000000e+00
#> 164 9.803926e-01 0.98038990 9.803858e-01
#> 165 8.399153e-02 0.08399119 8.398958e-02
#> 166 8.740665e-01 0.87406897 8.740663e-01
#> 167 0.000000e+00 0.00000000 0.000000e+00
#> 168 8.740665e-01 0.87406897 8.740663e-01
#> 169 8.399153e-02 0.08399119 8.398958e-02
#> 170 9.803926e-01 0.98038990 9.803858e-01
#> 171 8.399153e-02 0.08399119 8.398958e-02
#> 172 8.740665e-01 0.87406897 8.740663e-01
#> 173 8.399153e-02 0.08399119 8.398958e-02
#> 174 9.803926e-01 0.98038990 9.803858e-01
#> 175 9.803926e-01 0.98038990 9.803858e-01
#> 176 0.000000e+00 0.00000000 0.000000e+00
#> 177 9.803926e-01 0.98038990 9.803858e-01
#> 178 8.740665e-01 0.87406897 8.740663e-01
#> 179 0.000000e+00 0.00000000 0.000000e+00
#> 180 0.000000e+00 0.00000000 0.000000e+00
#> 181 8.399153e-02 0.08399119 8.398958e-02
#> 182 9.803926e-01 0.98038990 9.803858e-01
#> 183 8.399153e-02 0.08399119 8.398958e-02
#> 184 9.803926e-01 0.98038990 9.803858e-01
#> 185 0.000000e+00 0.00000000 0.000000e+00
#> 186 9.803926e-01 0.98038990 9.803858e-01
#> 187 8.740665e-01 0.87406897 8.740663e-01
#> 188 8.740665e-01 0.87406897 8.740663e-01
#> 189 9.803926e-01 0.98038990 9.803858e-01
#> 190 0.000000e+00 0.00000000 0.000000e+00
#> 191 9.803926e-01 0.98038990 9.803858e-01
#> 192 0.000000e+00 0.00000000 0.000000e+00
#> 193 0.000000e+00 0.00000000 0.000000e+00
#> 194 8.740665e-01 0.87406897 8.740663e-01
#> 195 8.399153e-02 0.08399119 8.398958e-02
#> 196 9.803926e-01 0.98038990 9.803858e-01
#> 197 8.740665e-01 0.87406897 8.740663e-01
#> 198 9.803926e-01 0.98038990 9.803858e-01
#> 199 8.740665e-01 0.87406897 8.740663e-01
#> 200 0.000000e+00 0.00000000 0.000000e+00
#> 201 8.740665e-01 0.87406897 8.740663e-01
#> 202 8.399153e-02 0.08399119 8.398958e-02
#> 203 8.740665e-01 0.87406897 8.740663e-01
#> 204 8.740665e-01 0.87406897 8.740663e-01
#> 205 8.399153e-02 0.08399119 8.398958e-02
#> 206 0.000000e+00 0.00000000 0.000000e+00
#> 207 9.803926e-01 0.98038990 9.803858e-01
#> 208 8.399153e-02 0.08399119 8.398958e-02
#> 209 0.000000e+00 0.00000000 0.000000e+00
#> 210 9.803926e-01 0.98038990 9.803858e-01
#> 211 8.399153e-02 0.08399119 8.398958e-02
#> 212 0.000000e+00 0.00000000 0.000000e+00
#> 213 9.803926e-01 0.98038990 9.803858e-01
#> 214 9.803926e-01 0.98038990 9.803858e-01
#> 215 9.803926e-01 0.98038990 9.803858e-01
#> 216 8.399153e-02 0.08399119 8.398958e-02
#> 217 8.399153e-02 0.08399119 8.398958e-02
#> 218 9.803926e-01 0.98038990 9.803858e-01
#> 219 0.000000e+00 0.00000000 0.000000e+00
#> 220 8.399153e-02 0.08399119 8.398958e-02
#> 221 0.000000e+00 0.00000000 0.000000e+00
#> 222 8.399153e-02 0.08399119 8.398958e-02
#> 223 8.740665e-01 0.87406897 8.740663e-01
#> 224 0.000000e+00 0.00000000 0.000000e+00
#> 225 8.740665e-01 0.87406897 8.740663e-01
#> 226 8.740665e-01 0.87406897 8.740663e-01
#> 227 0.000000e+00 0.00000000 0.000000e+00
#> 228 1.000000e+00 0.99977443 1.000000e+00
#> 229 9.803926e-01 0.98038990 9.803858e-01
#> 230 9.803926e-01 0.98038990 9.803858e-01
#> 231 0.000000e+00 0.00000000 0.000000e+00
#> 232 9.803926e-01 0.98038990 9.803858e-01
#> 233 0.000000e+00 0.00000000 0.000000e+00
#> 234 9.803926e-01 0.98038990 9.803858e-01
#> 235 0.000000e+00 0.00000000 0.000000e+00
#> 236 0.000000e+00 0.00000000 0.000000e+00
#> 237 8.399153e-02 0.08399119 8.398958e-02
#> 238 9.803926e-01 0.98038990 9.803858e-01
#> 239 9.803926e-01 0.98038990 9.803858e-01
#> 240 8.740665e-01 0.87406897 8.740663e-01
#> 241 5.931236e-05 0.00000000 0.000000e+00
#> 242 8.740665e-01 0.87406897 8.740663e-01
#> 243 9.803926e-01 0.98038990 9.803858e-01
#> 244 0.000000e+00 0.00000000 0.000000e+00
#> 245 8.399153e-02 0.08399119 8.398958e-02
#> 246 8.399153e-02 0.08399119 8.398958e-02
#> 247 8.399153e-02 0.08399119 8.398958e-02
#> 248 8.399153e-02 0.08399119 8.398958e-02
#> 249 9.803926e-01 0.98038990 9.803858e-01
#> 250 0.000000e+00 0.00000000 0.000000e+00
modpls.B2$MissClassed
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> [1,] 124 11 13 11 11 11 11 11 11
plsR(simbin1$Y,dataAstar2[,-1],10,typeVC="standard",MClassed=TRUE,verbose=FALSE)$InfCrit
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 366.87968 NA NA NA NA 62.49600
#> Nb_Comp_1 179.27607 0.52469907 0.0975 0.52469907 29.70441 29.27366
#> Nb_Comp_2 111.50920 0.63784869 0.0975 0.23805890 22.30480 22.14520
#> Nb_Comp_3 92.19471 0.61461931 0.0975 -0.06414274 23.56565 20.33539
#> Nb_Comp_4 92.80958 0.54850120 0.0975 -0.17156571 23.82425 20.22303
#> Nb_Comp_5 94.61150 0.46505988 0.0975 -0.18480962 23.96044 20.20702
#> Nb_Comp_6 96.58935 0.37811037 0.0975 -0.16254065 23.49148 20.20523
#> Nb_Comp_7 98.58715 0.28547861 0.0975 -0.14895208 23.21484 20.20505
#> Nb_Comp_8 100.58707 0.18505584 0.0975 -0.14054551 23.04478 20.20504
#> Nb_Comp_9 102.58707 0.07664463 0.0975 -0.13302900 22.89290 20.20504
#> Nb_Comp_10 104.58707 -0.03839400 0.0975 -0.12458760 22.72234 20.20504
#> R2_Y MissClassed R2_residY RSS_residY PRESS_residY Q2_residY
#> Nb_Comp_0 NA 124 NA 249.00000 NA NA
#> Nb_Comp_1 0.5315915 30 0.5315915 116.63372 118.34993 0.52469907
#> Nb_Comp_2 0.6456542 6 0.6456542 88.23211 88.86803 0.23805890
#> Nb_Comp_3 0.6746129 8 0.6746129 81.02138 93.89156 -0.06414274
#> Nb_Comp_4 0.6764108 11 0.6764108 80.57372 94.92187 -0.17156571
#> Nb_Comp_5 0.6766671 10 0.6766671 80.50990 95.46452 -0.18480962
#> Nb_Comp_6 0.6766957 10 0.6766957 80.50277 93.59604 -0.16254065
#> Nb_Comp_7 0.6766985 10 0.6766985 80.50206 92.49383 -0.14895208
#> Nb_Comp_8 0.6766986 10 0.6766986 80.50204 91.81627 -0.14054551
#> Nb_Comp_9 0.6766987 10 0.6766987 80.50203 91.21114 -0.13302900
#> Nb_Comp_10 0.6766987 10 0.6766987 80.50203 90.53159 -0.12458760
#> LimQ2 Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof
#> Nb_Comp_0 NA NA 712.4673 1.000000 0.5009870 0.25199190
#> Nb_Comp_1 0.0975 0.52469907 524.8637 2.621153 0.3433059 0.11956605
#> Nb_Comp_2 0.0975 0.63784869 457.0968 3.000068 0.2988230 0.09072392
#> Nb_Comp_3 0.0975 0.61461931 437.7823 21.497037 0.2976680 0.09657973
#> Nb_Comp_4 0.0975 0.54850120 438.3972 17.017307 0.2939891 0.09265852
#> Nb_Comp_5 0.0975 0.46505988 440.1991 17.406608 0.2941175 0.09287414
#> Nb_Comp_6 0.0975 0.37811037 442.1769 17.993023 0.2944743 0.09330304
#> Nb_Comp_7 0.0975 0.28547861 444.1747 18.501957 0.2947951 0.09368337
#> Nb_Comp_8 0.0975 0.18505584 446.1747 18.936590 0.2950710 0.09401017
#> Nb_Comp_9 0.0975 0.07664463 448.1747 19.634828 0.2955159 0.09453778
#> Nb_Comp_10 0.0975 -0.03839400 450.1747 19.983460 0.2957388 0.09480242
#> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive
#> Nb_Comp_0 0.2555273 -167.2823 1 0.5009870 0.25199190 0.25552728
#> Nb_Comp_1 0.1239175 -257.0188 2 0.3435680 0.11898326 0.12230862
#> Nb_Comp_2 0.0944974 -290.3040 3 0.2994272 0.09073255 0.09452122
#> Nb_Comp_3 0.1234100 -257.2238 4 0.2875138 0.08398681 0.08864439
#> Nb_Comp_4 0.1133760 -267.0758 5 0.2873030 0.08419385 0.09000729
#> Nb_Comp_5 0.1140840 -266.3413 6 0.2877771 0.08480321 0.09180238
#> Nb_Comp_6 0.1152807 -265.1168 7 0.2883558 0.08547725 0.09367583
#> Nb_Comp_7 0.1163320 -264.0546 8 0.2889497 0.08616367 0.09557211
#> Nb_Comp_8 0.1172342 -263.1526 9 0.2895485 0.08685653 0.09748493
#> Nb_Comp_9 0.1186908 -261.7148 10 0.2901511 0.08755518 0.09941372
#> Nb_Comp_10 0.1194214 -261.0020 11 0.2907575 0.08825968 0.10135865
#> GMDL.naive
#> Nb_Comp_0 -167.2823
#> Nb_Comp_1 -258.3373
#> Nb_Comp_2 -289.8052
#> Nb_Comp_3 -297.3647
#> Nb_Comp_4 -295.2257
#> Nb_Comp_5 -292.6062
#> Nb_Comp_6 -289.9866
#> Nb_Comp_7 -287.4310
#> Nb_Comp_8 -284.9391
#> Nb_Comp_9 -282.5049
#> Nb_Comp_10 -280.1229
cv.modpls.B2<-cv.plsR(Y~.,data=simbin1,2,NK=100,verbose=FALSE)
res.cv.modpls.B2<-cvtable(summary(cv.modpls.B2,MClassed=TRUE))
#> ____************************************************____
#> Error in eval(mf, parent.frame()): object 'simbin1' not found
#Only one component found by repeated CV missclassed criterion
plot(res.cv.modpls.B2)
#> Error: object 'res.cv.modpls.B2' not found
rm(list=c("dimX","Astar","dataAstar2","modpls.A2","cv.modpls.A2",
"res.cv.modpls.A2","simbin1","modpls.B2","cv.modpls.B2","res.cv.modpls.B2"))
#> Warning: object 'res.cv.modpls.A2' not found
#> Warning: object 'res.cv.modpls.B2' not found
# }