Fitting a LASSO/LARS model on the (Deviance) Residuals
Source:R/larsDR_coxph.R, R/larsDR_coxph.default.R, R/larsDR_coxph.formula.R
larsDR_coxph.RdThis function computes the Cox Model based on lars variables computed model with
as the response: the Residuals of a Cox-Model fitted with no covariate
as explanatory variables: Xplan.
It uses the
package lars to perform PLSR fit.
Usage
larsDR_coxph(Xplan, ...)
# Default S3 method
larsDR_coxph(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = FALSE,
scaleY = TRUE,
plot = FALSE,
typelars = "lasso",
normalize = TRUE,
max.steps,
use.Gram = TRUE,
allres = FALSE,
verbose = TRUE,
...
)
# S3 method for class 'formula'
larsDR_coxph(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = FALSE,
scaleY = TRUE,
plot = FALSE,
typelars = "lasso",
normalize = TRUE,
max.steps,
use.Gram = TRUE,
allres = FALSE,
dataXplan = NULL,
subset,
weights,
model_frame = FALSE,
model_matrix = FALSE,
verbose = TRUE,
contrasts.arg = NULL,
...
)Arguments
- Xplan
a formula or a matrix with the eXplanatory variables (training) dataset
- ...
Arguments to be passed on to
survival::coxphor tolars::lars.- time
for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.
- time2
The status indicator, normally 0=alive, 1=dead. Other choices are
TRUE/FALSE(TRUE= death) or 1/2 (2=death). For interval censored data, the status indicator is 0=right censored, 1=event attime, 2=left censored, 3=interval censored. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have an event.- event
ending time of the interval for interval censored or counting process data only. Intervals are assumed to be open on the left and closed on the right,
(start, end]. For counting process data, event indicates whether an event occurred at the end of the interval.- type
character string specifying the type of censoring. Possible values are
"right","left","counting","interval", or"interval2". The default is"right"or"counting"depending on whether thetime2argument is absent or present, respectively.- origin
for counting process data, the hazard function origin. This option was intended to be used in conjunction with a model containing time dependent strata in order to align the subjects properly when they cross over from one strata to another, but it has rarely proven useful.
- typeres
character string indicating the type of residual desired. Possible values are
"martingale","deviance","score","schoenfeld","dfbeta","dfbetas", and"scaledsch". Only enough of the string to determine a unique match is required.- collapse
vector indicating which rows to collapse (sum) over. In time-dependent models more than one row data can pertain to a single individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of data respectively, then
collapse=c(1,1,1,2,3,3,4,4,4,4)could be used to obtain per subject rather than per observation residuals.- weighted
if
TRUEand the model was fit with case weights, then the weighted residuals are returned.- scaleX
Should the
Xplancolumns be standardized ?- scaleY
Should the
timevalues be standardized ?- plot
Should the survival function be plotted ?)
- typelars
One of
"lasso","lar","forward.stagewise"or"stepwise". The names can be abbreviated to any unique substring. Default is"lasso".- normalize
If TRUE, each variable is standardized to have unit L2 norm, otherwise it is left alone. Default is TRUE.
- max.steps
Limit the number of steps taken; the default is
8 * min(m, n-intercept), with m the number of variables, and n the number of samples. Fortype="lar"ortype="stepwise", the maximum number of steps ismin(m,n-intercept). Fortype="lasso"and especiallytype="forward.stagewise", there can be many more terms, because although no more than min(m,n-intercept) variables can be active during any step, variables are frequently droppped and added as the algorithm proceeds. Although the default usually guarantees that the algorithm has proceeded to the saturated fit, users should check.- use.Gram
When the number m of variables is very large, i.e. larger than N, then you may not want LARS to precompute the Gram matrix. Default is
use.Gram=TRUE- allres
FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE.
- verbose
Should some details be displayed ?
- dataXplan
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 indataXplan, the variables are taken fromenvironment(Xplan), typically the environment from whichplscoxis called.- subset
an optional vector specifying a subset of observations to be used in the fitting process.
- weights
an optional vector of 'prior weights' to be used in the fitting process. Should be
NULLor a numeric vector.- model_frame
If
TRUE, the model frame is returned.- model_matrix
If
TRUE, the model matrix is returned.- contrasts.arg
a list, whose entries are values (numeric matrices, functions or character strings naming functions) to be used as replacement values for the contrasts replacement function and whose names are the names of columns of data containing factors.
Value
If allres=FALSE :
- cox_larsDR
Final Cox-model.
If
allres=TRUE :
- DR_coxph
The (Deviance) Residuals.
- larsDR
The LASSO/LARS model fitted to the (Deviance) Residuals.
- X_larsDR
The eXplanatory variables.
- cox_larsDR
Final Cox-model.
Details
This function computes the LASSO/LARS model with the Residuals of a Cox-Model fitted with an intercept as the only explanatory variable as the response and Xplan as explanatory variables. Default behaviour uses the Deviance residuals.
If allres=FALSE returns only the final Cox-model. If
allres=TRUE returns a list with the (Deviance) Residuals, the
LASSO/LARS model fitted to the (Deviance) Residuals, the eXplanatory
variables and the final Cox-model. allres=TRUE is useful for
evluating model prediction accuracy on a test sample.
References
plsRcox, Cox-Models in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.
Author
Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
Examples
data(micro.censure)
data(Xmicro.censure_compl_imp)
X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),FUN="as.numeric",MARGIN=2)[1:80,]
X_train_micro_df <- data.frame(X_train_micro)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
(cox_larsDR_fit <- larsDR_coxph(X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
#> Loaded lars 1.3
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> D20S107 0.6263 1.8706 0.4316 1.451 0.146794
#> D3S1282 -0.2857 0.7515 0.4547 -0.628 0.529826
#> D15S127 0.9521 2.5911 0.3834 2.483 0.013027
#> D2S138 -1.3850 0.2503 0.4197 -3.300 0.000967
#> D16S408 -0.7357 0.4792 0.3880 -1.896 0.057930
#> N 0.4274 1.5332 0.2675 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
(cox_larsDR_fit <- larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
#> Loaded lars 1.3
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> X_train_microD20S107 0.6263 1.8706 0.4316 1.451 0.146794
#> X_train_microD3S1282 -0.2857 0.7515 0.4547 -0.628 0.529826
#> X_train_microD15S127 0.9521 2.5911 0.3834 2.483 0.013027
#> X_train_microD2S138 -1.3850 0.2503 0.4197 -3.300 0.000967
#> X_train_microD16S408 -0.7357 0.4792 0.3880 -1.896 0.057930
#> X_train_microN 0.4274 1.5332 0.2675 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
(cox_larsDR_fit <- larsDR_coxph(~.,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE,dataXplan=X_train_micro_df))
#> Loaded lars 1.3
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> D20S107 0.6263 1.8706 0.4316 1.451 0.146794
#> D3S1282 -0.2857 0.7515 0.4547 -0.628 0.529826
#> D15S127 0.9521 2.5911 0.3834 2.483 0.013027
#> D2S138 -1.3850 0.2503 0.4197 -3.300 0.000967
#> D16S408 -0.7357 0.4792 0.3880 -1.896 0.057930
#> N 0.4274 1.5332 0.2675 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE)
#> Loaded lars 1.3
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> X_train_microD20S107 1.27033 3.56204 0.87552 1.451 0.146794
#> X_train_microD3S1282 -0.56794 0.56669 0.90398 -0.628 0.529826
#> X_train_microD15S127 1.89761 6.66992 0.76423 2.483 0.013027
#> X_train_microD2S138 -2.75342 0.06371 0.83440 -3.300 0.000967
#> X_train_microD16S408 -1.47895 0.22788 0.77994 -1.896 0.057930
#> X_train_microN 0.54943 1.73227 0.34390 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,scaleX=FALSE)
#> Loaded lars 1.3
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> X_train_microD20S107 1.27033 3.56204 0.87552 1.451 0.146794
#> X_train_microD3S1282 -0.56794 0.56669 0.90398 -0.628 0.529826
#> X_train_microD15S127 1.89761 6.66992 0.76423 2.483 0.013027
#> X_train_microD2S138 -2.75342 0.06371 0.83440 -3.300 0.000967
#> X_train_microD16S408 -1.47895 0.22788 0.77994 -1.896 0.057930
#> X_train_microN 0.54943 1.73227 0.34390 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,
scaleX=TRUE,allres=TRUE)
#> Loaded lars 1.3
#> $DR_coxph
#> 1 2 3 4 5 6
#> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660
#> 7 8 9 10 11 12
#> -0.38667660 1.57418914 -0.54695398 -0.15811388 2.10405254 -0.23145502
#> 13 14 15 16 17 18
#> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660
#> 19 20 21 22 23 24
#> 0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722
#> 25 26 27 28 29 30
#> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502
#> 31 32 33 34 35 36
#> 2.30072697 -0.49023986 -0.54695398 -0.73444882 1.31082939 -0.97633722
#> 37 38 39 40 41 42
#> 1.70134282 -0.54695398 -0.15811388 1.07714870 -0.15811388 -0.49023986
#> 43 44 45 46 47 48
#> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414
#> 49 50 51 52 53 54
#> -0.38667660 -0.09836581 -0.79392956 0.46851068 -0.34003013 1.95366297
#> 55 56 57 58 59 60
#> 2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722
#> 61 62 63 64 65 66
#> -0.28961087 1.44879795 1.82660327 -0.38667660 0.96936094 -0.15811388
#> 67 68 69 70 71 72
#> -0.43627414 -0.49023986 1.18850436 -0.97633722 -0.97633722 0.86322194
#> 73 74 75 76 77 78
#> -0.43627414 -0.49023986 -0.38667660 0.76231394 -0.97633722 -0.43627414
#> 79 80
#> -0.54695398 -0.43627414
#>
#> $larsDR
#>
#> Call:
#> lars(x = structure(c(-1.04472358469647, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, -1.21706614446381,
#> 0.811377429642539, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, -1.21706614446381,
#> -1.21706614446381, -1.21706614446381, -1.21706614446381, 0.811377429642539,
#> -1.21706614446381, -1.21706614446381, -1.21706614446381, -1.21706614446381,
#> 0.811377429642539, -1.21706614446381, -1.21706614446381, 0.811377429642539,
#> -1.21706614446381, -1.21706614446381, -1.21706614446381, 0.811377429642539,
#> -1.21706614446381, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, -1.21706614446381,
#> -1.21706614446381, 0.811377429642539, -1.21706614446381, -1.21706614446381,
#> -1.21706614446381, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, 0.811377429642539,
#> -1.21706614446381, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> -1.21706614446381, -1.21706614446381, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, -1.21706614446381,
#> -1.21706614446381, 0.811377429642539, -1.21706614446381, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, 0.811377429642539,
#> 0.811377429642539, 0.811377429642539, 0.811377429642539, 0.811377429642539,
#> -1.21706614446381, 0.921796779298767, -1.07127733810397, -1.07127733810397,
#> 0.921796779298767, 0.921796779298767, -1.07127733810397, 0.921796779298767,
#> -1.07127733810397, -1.07127733810397, 0.921796779298767, 0.921796779298767,
#> 0.921796779298767, -1.07127733810397, -1.07127733810397, 0.921796779298767,
#> -1.07127733810397, -1.07127733810397, -1.07127733810397, 0.921796779298767,
#> 0.921796779298767, -1.07127733810397, -1.07127733810397, -1.07127733810397,
#> 0.921796779298767, -1.07127733810397, 0.921796779298767, -1.07127733810397,
#> -1.07127733810397, -1.07127733810397, 0.921796779298767, 0.921796779298767,
#> 0.921796779298767, -1.07127733810397, 0.921796779298767, 0.921796779298767,
#> 0.921796779298767, 0.921796779298767, -1.07127733810397, -1.07127733810397,
#> -1.07127733810397, -1.07127733810397, 0.921796779298767, 0.921796779298767,
#> -1.07127733810397, -1.07127733810397, -1.07127733810397, -1.07127733810397,
#> 0.921796779298767, -1.07127733810397, 0.921796779298767, 0.921796779298767,
#> -1.07127733810397, -1.07127733810397, 0.921796779298767, -1.07127733810397,
#> 0.921796779298767, 0.921796779298767, 0.921796779298767, 0.921796779298767,
#> 0.921796779298767, 0.921796779298767, 0.921796779298767, 0.921796779298767,
#> -1.07127733810397, -1.07127733810397, 0.921796779298767, -1.07127733810397,
#> 0.921796779298767, 0.921796779298767, 0.921796779298767, 0.921796779298767,
#> -1.07127733810397, 0.921796779298767, 0.921796779298767, -1.07127733810397,
#> 0.921796779298767, 0.921796779298767, -1.07127733810397, 0.921796779298767,
#> -1.07127733810397, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.769740215917033, -0.769740215917033, 1.28290035986172,
#> 1.28290035986172, -0.769740215917033, 1.28290035986172, 1.28290035986172,
#> 1.28290035986172, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, 1.28290035986172, -0.769740215917033,
#> -0.769740215917033, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, -0.769740215917033, 1.28290035986172,
#> 1.28290035986172, 1.28290035986172, 1.28290035986172, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, -0.769740215917033, 1.28290035986172,
#> -0.769740215917033, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> 1.28290035986172, 1.28290035986172, 1.28290035986172, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, 1.28290035986172, -0.769740215917033,
#> -0.769740215917033, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> -0.769740215917033, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> 1.28290035986172, -0.769740215917033, -0.769740215917033, 1.28290035986172,
#> 1.28290035986172, 1.28290035986172, -0.769740215917033, 1.28290035986172,
#> -0.769740215917033, -0.769740215917033, 1.28290035986172, 1.28290035986172,
#> -0.769740215917033, -0.769740215917033, 1.28290035986172, -0.769740215917033,
#> -0.769740215917033, -0.769740215917033, -0.769740215917033, -0.769740215917033,
#> -0.769740215917033, 1.28290035986172, -0.769740215917033, 1.28290035986172,
#> -0.769740215917033, 0.99373034571759, -0.99373034571759, 0.99373034571759,
#> -0.99373034571759, 0.99373034571759, -0.99373034571759, 0.99373034571759,
#> 0.99373034571759, 0.99373034571759, 0.99373034571759, 0.99373034571759,
#> 0.99373034571759, 0.99373034571759, -0.99373034571759, 0.99373034571759,
#> -0.99373034571759, -0.99373034571759, -0.99373034571759, 0.99373034571759,
#> -0.99373034571759, -0.99373034571759, 0.99373034571759, -0.99373034571759,
#> -0.99373034571759, 0.99373034571759, -0.99373034571759, 0.99373034571759,
#> 0.99373034571759, -0.99373034571759, -0.99373034571759, -0.99373034571759,
#> -0.99373034571759, -0.99373034571759, -0.99373034571759, -0.99373034571759,
#> 0.99373034571759, 0.99373034571759, -0.99373034571759, -0.99373034571759,
#> 0.99373034571759, -0.99373034571759, 0.99373034571759, -0.99373034571759,
#> -0.99373034571759, -0.99373034571759, 0.99373034571759, -0.99373034571759,
#> -0.99373034571759, 0.99373034571759, 0.99373034571759, 0.99373034571759,
#> -0.99373034571759, 0.99373034571759, -0.99373034571759, -0.99373034571759,
#> -0.99373034571759, -0.99373034571759, -0.99373034571759, 0.99373034571759,
#> 0.99373034571759, 0.99373034571759, 0.99373034571759, 0.99373034571759,
#> -0.99373034571759, -0.99373034571759, 0.99373034571759, 0.99373034571759,
#> 0.99373034571759, 0.99373034571759, -0.99373034571759, 0.99373034571759,
#> 0.99373034571759, -0.99373034571759, 0.99373034571759, 0.99373034571759,
#> 0.99373034571759, -0.99373034571759, 0.99373034571759, -0.99373034571759,
#> -0.99373034571759, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, 1.07127733810397, -0.921796779298767, 1.07127733810397,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, 1.07127733810397,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, 1.07127733810397, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, 1.21706614446381,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, 1.21706614446381,
#> 1.21706614446381, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, -0.811377429642539, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, 1.21706614446381, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, 1.21706614446381, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, 1.12678429929728, -0.876387788342327, -0.876387788342327,
#> 1.12678429929728, 1.12678429929728, -0.876387788342327, 1.12678429929728,
#> 1.12678429929728, 1.12678429929728, -0.876387788342327, 1.12678429929728,
#> -0.876387788342327, -0.876387788342327, -0.876387788342327, 1.12678429929728,
#> 1.12678429929728, -0.876387788342327, 1.12678429929728, 1.12678429929728,
#> 1.12678429929728, -0.876387788342327, -0.876387788342327, -0.876387788342327,
#> -0.876387788342327, -0.876387788342327, -0.876387788342327, -0.876387788342327,
#> -0.876387788342327, -0.876387788342327, -0.876387788342327, -0.876387788342327,
#> -0.876387788342327, -0.876387788342327, -0.876387788342327, -0.876387788342327,
#> -0.876387788342327, 1.12678429929728, -0.876387788342327, -0.876387788342327,
#> 1.12678429929728, 1.12678429929728, 1.12678429929728, -0.876387788342327,
#> -0.876387788342327, 1.12678429929728, 1.12678429929728, -0.876387788342327,
#> 1.12678429929728, 1.12678429929728, 1.12678429929728, 1.12678429929728,
#> -0.876387788342327, 1.12678429929728, -0.876387788342327, 1.12678429929728,
#> 1.12678429929728, -0.876387788342327, -0.876387788342327, 1.12678429929728,
#> -0.876387788342327, -0.876387788342327, -0.876387788342327, 1.12678429929728,
#> -0.876387788342327, 1.12678429929728, -0.876387788342327, -0.876387788342327,
#> 1.12678429929728, 1.12678429929728, 1.12678429929728, 1.12678429929728,
#> 1.12678429929728, -0.876387788342327, -0.876387788342327, 1.12678429929728,
#> 1.12678429929728, -0.876387788342327, -0.876387788342327, -0.876387788342327,
#> -0.876387788342327, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, 1.01889205789476, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, 1.09861023317847, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> 1.09861023317847, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, -0.898862918055109,
#> -0.898862918055109, -0.898862918055109, 1.09861023317847, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, -0.898862918055109,
#> 1.09861023317847, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, 1.09861023317847, -0.898862918055109, 1.09861023317847,
#> 1.09861023317847, 1.09861023317847, 1.09861023317847, -0.898862918055109,
#> 1.09861023317847, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -0.898862918055109, -0.898862918055109, 1.09861023317847,
#> -0.898862918055109, -1.09861023317847, -1.09861023317847, -1.09861023317847,
#> -1.09861023317847, 0.898862918055109, 0.898862918055109, 0.898862918055109,
#> 0.898862918055109, -1.09861023317847, 0.898862918055109, 0.898862918055109,
#> 0.898862918055109, 0.898862918055109, 0.898862918055109, -1.09861023317847,
#> -1.09861023317847, 0.898862918055109, -1.09861023317847, 0.898862918055109,
#> 0.898862918055109, -1.09861023317847, 0.898862918055109, 0.898862918055109,
#> -1.09861023317847, 0.898862918055109, 0.898862918055109, 0.898862918055109,
#> -1.09861023317847, -1.09861023317847, 0.898862918055109, -1.09861023317847,
#> 0.898862918055109, -1.09861023317847, 0.898862918055109, 0.898862918055109,
#> 0.898862918055109, -1.09861023317847, -1.09861023317847, 0.898862918055109,
#> -1.09861023317847, -1.09861023317847, 0.898862918055109, 0.898862918055109,
#> -1.09861023317847, 0.898862918055109, 0.898862918055109, 0.898862918055109,
#> -1.09861023317847, 0.898862918055109, 0.898862918055109, -1.09861023317847,
#> -1.09861023317847, -1.09861023317847, 0.898862918055109, -1.09861023317847,
#> -1.09861023317847, -1.09861023317847, 0.898862918055109, 0.898862918055109,
#> 0.898862918055109, 0.898862918055109, 0.898862918055109, -1.09861023317847,
#> -1.09861023317847, -1.09861023317847, 0.898862918055109, -1.09861023317847,
#> 0.898862918055109, 0.898862918055109, -1.09861023317847, -1.09861023317847,
#> 0.898862918055109, 0.898862918055109, -1.09861023317847, -1.09861023317847,
#> 0.898862918055109, 0.898862918055109, -1.09861023317847, 0.898862918055109,
#> -1.09861023317847, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, 1.15586738502508, 1.15586738502508, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> 1.15586738502508, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> 1.15586738502508, 1.15586738502508, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> 1.15586738502508, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> 1.15586738502508, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> 1.15586738502508, -0.854336762844621, 1.15586738502508, 1.15586738502508,
#> 1.15586738502508, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> 1.15586738502508, 1.15586738502508, -0.854336762844621, -0.854336762844621,
#> -0.854336762844621, 1.15586738502508, -0.854336762844621, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> 1.15586738502508, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, -0.854336762844621, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, 1.15586738502508, -0.854336762844621,
#> -0.854336762844621, -0.854336762844621, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.854336762844621, -0.854336762844621, 1.15586738502508,
#> -0.854336762844621, 1.15586738502508, 1.15586738502508, -0.854336762844621,
#> 1.15586738502508, 1.15586738502508, 1.15586738502508, 1.15586738502508,
#> -0.854336762844621, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> 1.04472358469647, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, 1.04472358469647, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> 1.04472358469647, -0.945226100439667, 1.04472358469647, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, -0.945226100439667, -0.945226100439667,
#> -0.945226100439667, -0.945226100439667, 1.04472358469647, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, 1.04472358469647,
#> -0.945226100439667, 1.04472358469647, -0.945226100439667, -0.945226100439667,
#> 1.04472358469647, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, -0.921796779298767, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, 1.07127733810397, -0.921796779298767, 1.07127733810397,
#> 1.07127733810397, 1.07127733810397, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, 1.07127733810397, 1.07127733810397, 1.07127733810397,
#> -0.921796779298767, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, -0.921796779298767,
#> 1.07127733810397, -0.921796779298767, -0.921796779298767, -0.921796779298767,
#> -0.921796779298767, -0.921796779298767, 1.07127733810397, 1.07127733810397,
#> 1.07127733810397, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> 0.945226100439667, 0.945226100439667, 0.945226100439667, -1.04472358469647,
#> -1.04472358469647, 0.945226100439667, 0.945226100439667, 0.945226100439667,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, 0.945226100439667, -1.04472358469647, 0.945226100439667,
#> -1.04472358469647, 0.945226100439667, -1.04472358469647, -1.04472358469647,
#> -1.04472358469647, -1.04472358469647, 0.945226100439667, 0.945226100439667,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, -1.04472358469647, -1.04472358469647, -1.04472358469647,
#> 0.945226100439667, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, 0.969190006290141, 0.969190006290141, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> -1.01889205789476, 0.969190006290141, -1.01889205789476, -1.01889205789476,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, -1.01889205789476,
#> 0.969190006290141, 0.969190006290141, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, 0.969190006290141,
#> -1.01889205789476, -1.01889205789476, -1.01889205789476, 0.969190006290141,
#> 0.969190006290141, -1.01889205789476, 0.969190006290141, -1.01889205789476,
#> 0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, 1.01889205789476, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, -0.969190006290141, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, 1.21706614446381,
#> 1.21706614446381, 1.21706614446381, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, -0.811377429642539,
#> 1.21706614446381, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, 1.21706614446381,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> -0.811377429642539, 1.21706614446381, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, 1.21706614446381,
#> 1.21706614446381, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, 1.21706614446381, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, -0.811377429642539, 1.21706614446381, 1.21706614446381,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, -0.811377429642539,
#> -0.811377429642539, -0.811377429642539, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, 1.21706614446381, 1.21706614446381, -0.811377429642539,
#> 1.21706614446381, -0.811377429642539, -0.811377429642539, 1.21706614446381,
#> -0.811377429642539, 0.854336762844621, -1.15586738502508, -1.15586738502508,
#> -1.15586738502508, -1.15586738502508, 0.854336762844621, -1.15586738502508,
#> -1.15586738502508, 0.854336762844621, -1.15586738502508, -1.15586738502508,
#> 0.854336762844621, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> -1.15586738502508, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> 0.854336762844621, -1.15586738502508, 0.854336762844621, -1.15586738502508,
#> -1.15586738502508, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> 0.854336762844621, 0.854336762844621, -1.15586738502508, 0.854336762844621,
#> -1.15586738502508, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> 0.854336762844621, -1.15586738502508, 0.854336762844621, -1.15586738502508,
#> -1.15586738502508, 0.854336762844621, -1.15586738502508, 0.854336762844621,
#> 0.854336762844621, 0.854336762844621, -1.15586738502508, -1.15586738502508,
#> -1.15586738502508, -1.15586738502508, -1.15586738502508, 0.854336762844621,
#> -1.15586738502508, 0.854336762844621, -1.15586738502508, -1.15586738502508,
#> 0.854336762844621, 0.854336762844621, -1.15586738502508, 0.854336762844621,
#> 0.854336762844621, -1.15586738502508, 0.854336762844621, -1.15586738502508,
#> 0.854336762844621, -1.15586738502508, 0.854336762844621, 0.854336762844621,
#> -1.15586738502508, -1.15586738502508, 0.854336762844621, -1.15586738502508,
#> 0.854336762844621, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> -1.15586738502508, 0.854336762844621, 0.854336762844621, 0.854336762844621,
#> 0.854336762844621, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> -0.969190006290141, 1.01889205789476, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, 1.01889205789476,
#> -0.969190006290141, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, 1.01889205789476, -0.969190006290141,
#> 1.01889205789476, 1.01889205789476, 1.01889205789476, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> 1.01889205789476, 1.01889205789476, -0.969190006290141, -0.969190006290141,
#> -0.969190006290141, -0.969190006290141, -0.969190006290141, -0.969190006290141,
#> 1.01889205789476, -0.969190006290141, -0.969190006290141, 1.01889205789476,
#> -0.969190006290141, 1.18593397537105, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, -0.832677046537123, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, 1.18593397537105, -0.832677046537123, 1.18593397537105,
#> 1.18593397537105, -0.832677046537123, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, -0.832677046537123, -0.832677046537123, -0.832677046537123,
#> 1.18593397537105, 1.18593397537105, 1.18593397537105, -0.832677046537123,
#> -0.832677046537123, 1.18593397537105, 1.18593397537105, -0.832677046537123,
#> 1.18593397537105, 1.18593397537105, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, 1.18593397537105, -0.832677046537123, -0.832677046537123,
#> -0.832677046537123, -0.832677046537123, 1.18593397537105, 1.18593397537105,
#> 1.18593397537105, 1.18593397537105, -0.832677046537123, 1.18593397537105,
#> 1.18593397537105, 1.18593397537105, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, -0.832677046537123, -0.832677046537123, -0.832677046537123,
#> -0.832677046537123, 1.18593397537105, -0.832677046537123, -0.832677046537123,
#> 1.18593397537105, -0.832677046537123, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, -0.832677046537123, -0.832677046537123, 1.18593397537105,
#> -0.832677046537123, -0.832677046537123, 1.18593397537105, -0.832677046537123,
#> 1.18593397537105, -0.832677046537123, -0.832677046537123, 1.18593397537105,
#> 1.18593397537105, -0.832677046537123, -0.832677046537123, -0.832677046537123,
#> -0.832677046537123, -0.832677046537123, -0.832677046537123, -0.832677046537123,
#> 1.18593397537105, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, -1.51794817654183, -1.51794817654183,
#> 0.650549218517927, -1.51794817654183, 0.650549218517927, -1.51794817654183,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, -1.51794817654183,
#> -1.51794817654183, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> -1.51794817654183, 0.650549218517927, -1.51794817654183, -1.51794817654183,
#> -1.51794817654183, -1.51794817654183, -1.51794817654183, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, -1.51794817654183, -1.51794817654183, -1.51794817654183,
#> 0.650549218517927, -1.51794817654183, 0.650549218517927, 0.650549218517927,
#> -1.51794817654183, -1.51794817654183, 0.650549218517927, -1.51794817654183,
#> -1.51794817654183, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, -1.51794817654183, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, -1.51794817654183,
#> 0.650549218517927, -1.51794817654183, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> -1.51794817654183, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, 0.650549218517927, 0.650549218517927, 0.650549218517927,
#> 0.650549218517927, -2.84243689529752, -0.229732314916899, 0.752085699177805,
#> 0.747216426060813, -0.412542971788335, -0.660279738799205, -0.371354475522511,
#> -0.451701555109114, 0.423595277532238, -2.22197745181848, -1.64575026316205,
#> 0.626898353631099, -1.35378198190243, 1.12196679375246, -1.33795591855488,
#> -0.115704676837868, 1.67526655633604, -0.0568646026642827, 1.55860099417447,
#> 0.215828762790623, -1.21601450822822, -0.154660342921527, 0.766491341865523,
#> 0.852317186854333, 0.329045472495037, 0.386870960483027, 0.965938990459126,
#> 0.965938990459126, 1.32770413612288, 1.19379208995393, 0.77237520116811,
#> -0.6328888740836, -1.56824402489511, 0.787389595567184, -2.95707328508791,
#> 0.137713772812327, 1.81039610592771, 0.020033624465158, 0.513478726689566,
#> -0.247587550635636, 0.812346194011221, -0.994856936984477, -0.476658153553151,
#> -1.28824563890393, 0.893910777020537, -0.175762254434165, -0.991002250053078,
#> 0.715970133839951, -0.567556188896072, -2.25667333707036, -1.12065303424254,
#> 0.312610657436132, -0.522513005698849, 0.726926183496649, 0.842374982809371,
#> -0.779380307806205, -0.919987882226224, -0.337876834267201, -1.05937795322353,
#> -0.171095157980433, 1.22280703314034, 0.756144043920181, 1.01848937085589,
#> 0.162061619545008, 0.410001303792996, 0.816200880942621, 1.3912113067502,
#> 0.325799537274995, 0.688173434650109, 0.272640405166878, -1.16468163125417,
#> 0.391335139699641, 0.14968440865462, 0.566434941560564, 0.22840889091813,
#> -0.105965390030024, -0.0840525501427683, 0.244640788739918, 0.651854952075136,
#> -0.0580821060869957, -1.03904847299718, 0.301659234095955, 0.301659234095955,
#> 0.301659234095955, 0.301659234095955, 0.301659234095955, 0.301659234095955,
#> 0.301659234095955, 0.301659234095955, -1.03904847299718, -1.03904847299718,
#> 0.301659234095955, 0.301659234095955, -1.03904847299718, 0.301659234095955,
#> -1.03904847299718, 0.301659234095955, -1.03904847299718, 0.301659234095955,
#> -1.03904847299718, -1.03904847299718, 1.64236694118909, -1.03904847299718,
#> -1.03904847299718, 1.64236694118909, -1.03904847299718, 0.301659234095955,
#> 0.301659234095955, -1.03904847299718, -1.03904847299718, 1.64236694118909,
#> 1.64236694118909, 0.301659234095955, -1.03904847299718, 0.301659234095955,
#> 0.301659234095955, 0.301659234095955, -1.03904847299718, -1.03904847299718,
#> 1.64236694118909, 0.301659234095955, -1.03904847299718, 0.301659234095955,
#> 1.64236694118909, -1.03904847299718, 0.301659234095955, 0.301659234095955,
#> 1.64236694118909, -1.03904847299718, -1.03904847299718, 0.301659234095955,
#> 1.64236694118909, 0.301659234095955, -1.03904847299718, -1.03904847299718,
#> -1.03904847299718, -1.03904847299718, -1.03904847299718, -1.03904847299718,
#> -1.03904847299718, -1.03904847299718, 0.301659234095955, 1.64236694118909,
#> 1.64236694118909, -1.03904847299718, -1.03904847299718, -1.03904847299718,
#> 0.301659234095955, 0.301659234095955, 1.64236694118909, 1.64236694118909,
#> -1.03904847299718, -1.03904847299718, 0.301659234095955, 0.301659234095955,
#> 0.301659234095955, 1.64236694118909, 1.64236694118909, 0.301659234095955,
#> 1.64236694118909, 0.541677248057095, -2.79172120152503, 0.541677248057095,
#> -1.68058838499765, 0.541677248057095, 0.541677248057095, -1.68058838499765,
#> 0.541677248057095, 0.541677248057095, -2.79172120152503, 0.541677248057095,
#> 0.541677248057095, 0.541677248057095, 0.541677248057095, 0.541677248057095,
#> 0.541677248057095, -0.56945556847028, 0.541677248057095, 0.541677248057095,
#> 0.541677248057095, -1.68058838499765, 0.541677248057095, 0.541677248057095,
#> -0.56945556847028, 0.541677248057095, -0.56945556847028, 0.541677248057095,
#> 0.541677248057095, -0.56945556847028, -1.68058838499765, -0.56945556847028,
#> -2.79172120152503, -0.56945556847028, 1.65281006458447, 1.65281006458447,
#> 0.541677248057095, 0.541677248057095, -0.56945556847028, -0.56945556847028,
#> 0.541677248057095, -1.68058838499765, 0.541677248057095, -1.68058838499765,
#> -0.56945556847028, 0.541677248057095, 0.541677248057095, 0.541677248057095,
#> -1.68058838499765, 0.541677248057095, 0.541677248057095, -0.56945556847028,
#> 0.541677248057095, 0.541677248057095, -0.56945556847028, 0.541677248057095,
#> -0.56945556847028, -0.56945556847028, -0.56945556847028, -1.68058838499765,
#> 0.541677248057095, 0.541677248057095, 0.541677248057095, 1.65281006458447,
#> 0.541677248057095, 0.541677248057095, 0.541677248057095, 0.541677248057095,
#> 0.541677248057095, 0.541677248057095, 0.541677248057095, -1.68058838499765,
#> 1.65281006458447, 0.541677248057095, -0.56945556847028, 0.541677248057095,
#> 0.541677248057095, 0.541677248057095, 0.541677248057095, 0.541677248057095,
#> -0.56945556847028, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> 1.86409310288792, 0.578511652620389, -0.707069797647142, 1.86409310288792,
#> -0.707069797647142, 1.86409310288792, -0.707069797647142, 1.86409310288792,
#> 1.86409310288792, 0.578511652620389, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> 1.86409310288792, -0.707069797647142, -0.707069797647142, 1.86409310288792,
#> 1.86409310288792, -0.707069797647142, -0.707069797647142, 0.578511652620389,
#> -0.707069797647142, -0.707069797647142, 0.578511652620389, 1.86409310288792,
#> -0.707069797647142, 0.578511652620389, 0.578511652620389, -0.707069797647142,
#> 0.578511652620389, -0.707069797647142, 1.86409310288792, -0.707069797647142,
#> -0.707069797647142, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, 0.578511652620389, -0.707069797647142, -0.707069797647142,
#> 1.86409310288792, 0.578511652620389, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, 0.578511652620389, 0.578511652620389, 1.86409310288792,
#> -0.707069797647142, 0.578511652620389, -0.707069797647142, -0.707069797647142,
#> 1.86409310288792, -0.707069797647142, 0.578511652620389, -0.707069797647142,
#> 0.578511652620389, 0.578511652620389, -0.707069797647142, 0.578511652620389,
#> 1.86409310288792, -0.707069797647142, -0.707069797647142, -0.707069797647142,
#> -0.707069797647142, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, 1.72119144780585, 1.72119144780585, -0.57373048260195,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, 1.72119144780585,
#> -0.57373048260195, 1.72119144780585, -0.57373048260195, -0.57373048260195,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, 1.72119144780585, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, 1.72119144780585,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, 1.72119144780585,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, 1.72119144780585, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, 1.72119144780585, -0.57373048260195,
#> 1.72119144780585, 1.72119144780585, -0.57373048260195, -0.57373048260195,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, 1.72119144780585,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, 1.72119144780585, -0.57373048260195, 1.72119144780585,
#> 1.72119144780585, -0.57373048260195, -0.57373048260195, -0.57373048260195,
#> -0.57373048260195, -0.298525816034996, -1.94556480105567, -0.298525816034996,
#> -1.12204530854533, 1.34851316898567, 1.34851316898567, -1.12204530854533,
#> 1.34851316898567, -0.298525816034996, -1.94556480105567, 1.34851316898567,
#> -0.298525816034996, 1.34851316898567, -0.298525816034996, 0.524993676475338,
#> 1.34851316898567, 0.524993676475338, -0.298525816034996, -0.298525816034996,
#> -0.298525816034996, -1.12204530854533, 1.34851316898567, -0.298525816034996,
#> 0.524993676475338, -0.298525816034996, -1.12204530854533, 1.34851316898567,
#> 1.34851316898567, -1.12204530854533, -1.12204530854533, 0.524993676475338,
#> -1.94556480105567, -1.12204530854533, 0.524993676475338, 1.34851316898567,
#> -0.298525816034996, 0.524993676475338, 0.524993676475338, -1.12204530854533,
#> 0.524993676475338, -1.12204530854533, 1.34851316898567, -1.12204530854533,
#> -1.12204530854533, -0.298525816034996, 1.34851316898567, -0.298525816034996,
#> 1.34851316898567, 1.34851316898567, -0.298525816034996, -1.12204530854533,
#> 1.34851316898567, 0.524993676475338, -1.12204530854533, -0.298525816034996,
#> -1.12204530854533, -1.12204530854533, -1.12204530854533, -1.12204530854533,
#> -0.298525816034996, 0.524993676475338, 0.524993676475338, 1.34851316898567,
#> 1.34851316898567, 0.524993676475338, -0.298525816034996, -0.298525816034996,
#> 0.524993676475338, -0.298525816034996, 0.524993676475338, -1.12204530854533,
#> 0.524993676475338, 1.34851316898567, -1.12204530854533, 1.34851316898567,
#> 1.34851316898567, -0.298525816034996, -0.298525816034996, -0.298525816034996,
#> -1.12204530854533), dim = c(80L, 40L), assign = c(1L, 1L, 1L,
#> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
#> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
#> 1L, 1L, 1L, 1L, 1L), dimnames = list(c("1", "2", "3", "4", "5",
#> "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16",
#> "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
#> "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38",
#> "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49",
#> "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60",
#> "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71",
#> "72", "73", "74", "75", "76", "77", "78", "79", "80"), c("X_train_microD18S61",
#> "X_train_microD17S794", "X_train_microD13S173", "X_train_microD20S107",
#> "X_train_microTP53", "X_train_microD9S171", "X_train_microD8S264",
#> "X_train_microD5S346", "X_train_microD22S928", "X_train_microD18S53",
#> "X_train_microD1S225", "X_train_microD3S1282", "X_train_microD15S127",
#> "X_train_microD1S305", "X_train_microD1S207", "X_train_microD2S138",
#> "X_train_microD16S422", "X_train_microD9S179", "X_train_microD10S191",
#> "X_train_microD4S394", "X_train_microD1S197", "X_train_microD6S264",
#> "X_train_microD14S65", "X_train_microD17S790", "X_train_microD5S430",
#> "X_train_microD3S1283", "X_train_microD4S414", "X_train_microD8S283",
#> "X_train_microD11S916", "X_train_microD2S159", "X_train_microD16S408",
#> "X_train_microD6S275", "X_train_microD10S192", "X_train_microsexe",
#> "X_train_microAgediag", "X_train_microSiege", "X_train_microT",
#> "X_train_microN", "X_train_microM", "X_train_microSTADE")), "`scaled:center`" = c(X_train_microD18S61 = 0.525,
#> X_train_microD17S794 = 0.45, X_train_microD13S173 = 0.475, X_train_microD20S107 = 0.6,
#> X_train_microTP53 = 0.5375, X_train_microD9S171 = 0.475, X_train_microD8S264 = 0.525,
#> X_train_microD5S346 = 0.475, X_train_microD22S928 = 0.375, X_train_microD18S53 = 0.5,
#> X_train_microD1S225 = 0.4625, X_train_microD3S1282 = 0.5125,
#> X_train_microD15S127 = 0.4625, X_train_microD1S305 = 0.4, X_train_microD1S207 = 0.4375,
#> X_train_microD2S138 = 0.4875, X_train_microD16S422 = 0.45, X_train_microD9S179 = 0.5125,
#> X_train_microD10S191 = 0.5125, X_train_microD4S394 = 0.5125,
#> X_train_microD1S197 = 0.45, X_train_microD6S264 = 0.55, X_train_microD14S65 = 0.425,
#> X_train_microD17S790 = 0.425, X_train_microD5S430 = 0.475, X_train_microD3S1283 = 0.4625,
#> X_train_microD4S414 = 0.525, X_train_microD8S283 = 0.5125, X_train_microD11S916 = 0.4875,
#> X_train_microD2S159 = 0.4, X_train_microD16S408 = 0.575, X_train_microD6S275 = 0.4875,
#> X_train_microD10S192 = 0.4125, X_train_microsexe = 0.7, X_train_microAgediag = 64.236335125,
#> X_train_microSiege = 1.775, X_train_microT = 2.5125, X_train_microN = 0.55,
#> X_train_microM = 0.25, X_train_microSTADE = 2.3625), "`scaled:scale`" = c(X_train_microD18S61 = 0.502525268588178,
#> X_train_microD17S794 = 0.500632511321833, X_train_microD13S173 = 0.502525268588178,
#> X_train_microD20S107 = 0.492988818010657, X_train_microTP53 = 0.501737487466418,
#> X_train_microD9S171 = 0.502525268588178, X_train_microD8S264 = 0.502525268588178,
#> X_train_microD5S346 = 0.502525268588178, X_train_microD22S928 = 0.487177351846223,
#> X_train_microD18S53 = 0.503154605426628, X_train_microD1S225 = 0.501737487466418,
#> X_train_microD3S1282 = 0.502997345036655, X_train_microD15S127 = 0.501737487466418,
#> X_train_microD1S305 = 0.492988818010657, X_train_microD1S207 = 0.499208233865883,
#> X_train_microD2S138 = 0.502997345036655, X_train_microD16S422 = 0.500632511321833,
#> X_train_microD9S179 = 0.502997345036655, X_train_microD10S191 = 0.502997345036655,
#> X_train_microD4S394 = 0.502997345036655, X_train_microD1S197 = 0.500632511321833,
#> X_train_microD6S264 = 0.500632511321833, X_train_microD14S65 = 0.497461912542437,
#> X_train_microD17S790 = 0.497461912542437, X_train_microD5S430 = 0.502525268588178,
#> X_train_microD3S1283 = 0.501737487466418, X_train_microD4S414 = 0.502525268588178,
#> X_train_microD8S283 = 0.502997345036655, X_train_microD11S916 = 0.502997345036655,
#> X_train_microD2S159 = 0.492988818010657, X_train_microD16S408 = 0.497461912542437,
#> X_train_microD6S275 = 0.502997345036655, X_train_microD10S192 = 0.495390141610694,
#> X_train_microsexe = 0.461148813126632, X_train_microAgediag = 13.5030421215323,
#> X_train_microSiege = 0.745874730718269, X_train_microT = 0.89998241895627,
#> X_train_microN = 0.77785814332643, X_train_microM = 0.435744670330595,
#> X_train_microSTADE = 1.21430034030124)), y = c(`1` = -1.48432960272689,
#> `2` = -0.546953975803827, `3` = -0.231455024943138, `4` = -0.340030131930231,
#> `5` = -0.976337221215795, `6` = -0.386676603514146, `7` = -0.386676603514146,
#> `8` = 1.57418914279605, `9` = -0.546953975803827, `10` = -0.158113883008419,
#> `11` = 2.10405253987611, `12` = -0.231455024943138, `13` = -0.386676603514146,
#> `14` = -1.09692040255042, `15` = -0.158113883008419, `16` = -0.158113883008419,
#> `17` = -0.546953975803827, `18` = -0.386676603514146, `19` = 0.659786090690449,
#> `20` = -1.09692040255042, `21` = -0.436274136565358, `22` = -0.28961087492437,
#> `23` = -0.386676603514146, `24` = -0.976337221215795, `25` = -1.09692040255042,
#> `26` = -0.158113883008419, `27` = -0.436274136565358, `28` = -0.436274136565358,
#> `29` = -0.386676603514146, `30` = -0.231455024943138, `31` = 2.30072697072586,
#> `32` = -0.490239861940916, `33` = -0.546953975803827, `34` = -0.73444881503344,
#> `35` = 1.31082938694368, `36` = -0.976337221215795, `37` = 1.70134282314234,
#> `38` = -0.546953975803827, `39` = -0.158113883008419, `40` = 1.0771486960837,
#> `41` = -0.158113883008419, `42` = -0.490239861940916, `43` = -0.340030131930231,
#> `44` = -0.976337221215795, `45` = -0.158113883008419, `46` = -0.914104649757265,
#> `47` = -1.09692040255042, `48` = -0.436274136565358, `49` = -0.386676603514146,
#> `50` = -0.0983658134866668, `51` = -0.793929564138479, `52` = 0.468510675262855,
#> `53` = -0.340030131930231, `54` = 1.9536629749367, `55` = 2.60558117688699,
#> `56` = -0.546953975803827, `57` = -1.09692040255042, `58` = -0.158113883008419,
#> `59` = -0.490239861940916, `60` = -0.976337221215795, `61` = -0.28961087492437,
#> `62` = 1.4487979466996, `63` = 1.82660326764499, `64` = -0.386676603514146,
#> `65` = 0.969360944759548, `66` = -0.158113883008419, `67` = -0.436274136565358,
#> `68` = -0.490239861940916, `69` = 1.18850435510435, `70` = -0.976337221215795,
#> `71` = -0.976337221215795, `72` = 0.863221936022845, `73` = -0.436274136565358,
#> `74` = -0.490239861940916, `75` = -0.386676603514146, `76` = 0.762313939494724,
#> `77` = -0.976337221215795, `78` = -0.436274136565358, `79` = -0.546953975803827,
#> `80` = -0.436274136565358), type = "lasso", max.steps = 6, use.Gram = FALSE)
#> R-squared: 0.187
#> Sequence of LASSO moves:
#> X_train_microN X_train_microD3S1282 X_train_microD16S408
#> Var 38 12 31
#> Step 1 2 3
#> X_train_microD20S107 X_train_microD15S127 X_train_microD2S138
#> Var 4 13 16
#> Step 4 5 6
#>
#> $X_larsDR
#> X_train_microD20S107 X_train_microD3S1282 X_train_microD15S127
#> 1 -1.2170661 -1.018892 -0.9217968
#> 2 0.8113774 0.969190 -0.9217968
#> 3 0.8113774 -1.018892 -0.9217968
#> 4 0.8113774 -1.018892 1.0712773
#> 5 0.8113774 -1.018892 1.0712773
#> 6 0.8113774 0.969190 1.0712773
#> 7 -1.2170661 -1.018892 1.0712773
#> 8 0.8113774 -1.018892 -0.9217968
#> 9 -1.2170661 0.969190 -0.9217968
#> 10 0.8113774 -1.018892 -0.9217968
#> 11 0.8113774 -1.018892 1.0712773
#> 12 0.8113774 0.969190 1.0712773
#> 13 0.8113774 0.969190 -0.9217968
#> 14 0.8113774 0.969190 1.0712773
#> 15 -1.2170661 0.969190 -0.9217968
#> 16 -1.2170661 0.969190 1.0712773
#> 17 -1.2170661 0.969190 -0.9217968
#> 18 -1.2170661 0.969190 -0.9217968
#> 19 0.8113774 0.969190 -0.9217968
#> 20 -1.2170661 0.969190 -0.9217968
#> 21 -1.2170661 -1.018892 1.0712773
#> 22 -1.2170661 0.969190 1.0712773
#> 23 -1.2170661 -1.018892 -0.9217968
#> 24 0.8113774 -1.018892 1.0712773
#> 25 -1.2170661 0.969190 -0.9217968
#> 26 -1.2170661 -1.018892 1.0712773
#> 27 0.8113774 -1.018892 1.0712773
#> 28 -1.2170661 -1.018892 1.0712773
#> 29 -1.2170661 0.969190 1.0712773
#> 30 -1.2170661 0.969190 1.0712773
#> 31 0.8113774 -1.018892 1.0712773
#> 32 -1.2170661 0.969190 1.0712773
#> 33 -1.2170661 0.969190 -0.9217968
#> 34 0.8113774 0.969190 -0.9217968
#> 35 0.8113774 -1.018892 1.0712773
#> 36 0.8113774 -1.018892 -0.9217968
#> 37 0.8113774 -1.018892 1.0712773
#> 38 0.8113774 0.969190 1.0712773
#> 39 -1.2170661 0.969190 1.0712773
#> 40 -1.2170661 0.969190 1.0712773
#> 41 0.8113774 0.969190 1.0712773
#> 42 -1.2170661 -1.018892 -0.9217968
#> 43 -1.2170661 0.969190 1.0712773
#> 44 -1.2170661 -1.018892 1.0712773
#> 45 -1.2170661 0.969190 -0.9217968
#> 46 0.8113774 -1.018892 1.0712773
#> 47 0.8113774 -1.018892 -0.9217968
#> 48 0.8113774 0.969190 -0.9217968
#> 49 0.8113774 -1.018892 1.0712773
#> 50 0.8113774 -1.018892 -0.9217968
#> 51 0.8113774 0.969190 -0.9217968
#> 52 -1.2170661 -1.018892 -0.9217968
#> 53 -1.2170661 0.969190 -0.9217968
#> 54 0.8113774 -1.018892 1.0712773
#> 55 0.8113774 -1.018892 1.0712773
#> 56 -1.2170661 -1.018892 -0.9217968
#> 57 -1.2170661 0.969190 -0.9217968
#> 58 0.8113774 0.969190 1.0712773
#> 59 0.8113774 -1.018892 1.0712773
#> 60 0.8113774 0.969190 1.0712773
#> 61 0.8113774 -1.018892 -0.9217968
#> 62 0.8113774 -1.018892 -0.9217968
#> 63 0.8113774 -1.018892 1.0712773
#> 64 0.8113774 -1.018892 -0.9217968
#> 65 0.8113774 -1.018892 -0.9217968
#> 66 0.8113774 -1.018892 -0.9217968
#> 67 -1.2170661 0.969190 -0.9217968
#> 68 -1.2170661 0.969190 -0.9217968
#> 69 0.8113774 -1.018892 -0.9217968
#> 70 -1.2170661 0.969190 -0.9217968
#> 71 0.8113774 -1.018892 1.0712773
#> 72 0.8113774 0.969190 1.0712773
#> 73 0.8113774 -1.018892 -0.9217968
#> 74 0.8113774 0.969190 -0.9217968
#> 75 0.8113774 0.969190 -0.9217968
#> 76 0.8113774 -1.018892 1.0712773
#> 77 0.8113774 0.969190 -0.9217968
#> 78 0.8113774 0.969190 -0.9217968
#> 79 0.8113774 0.969190 -0.9217968
#> 80 -1.2170661 0.969190 -0.9217968
#> X_train_microD2S138 X_train_microD16S408 X_train_microN
#> 1 1.018892 0.8543368 -0.7070698
#> 2 -0.969190 -1.1558674 -0.7070698
#> 3 -0.969190 -1.1558674 -0.7070698
#> 4 1.018892 -1.1558674 -0.7070698
#> 5 1.018892 -1.1558674 -0.7070698
#> 6 1.018892 0.8543368 -0.7070698
#> 7 1.018892 -1.1558674 -0.7070698
#> 8 -0.969190 -1.1558674 1.8640931
#> 9 1.018892 0.8543368 0.5785117
#> 10 -0.969190 -1.1558674 -0.7070698
#> 11 1.018892 -1.1558674 1.8640931
#> 12 1.018892 0.8543368 -0.7070698
#> 13 -0.969190 0.8543368 1.8640931
#> 14 1.018892 0.8543368 -0.7070698
#> 15 -0.969190 0.8543368 1.8640931
#> 16 1.018892 -1.1558674 1.8640931
#> 17 1.018892 0.8543368 0.5785117
#> 18 1.018892 0.8543368 -0.7070698
#> 19 -0.969190 0.8543368 -0.7070698
#> 20 1.018892 0.8543368 -0.7070698
#> 21 1.018892 -1.1558674 -0.7070698
#> 22 1.018892 0.8543368 -0.7070698
#> 23 -0.969190 -1.1558674 -0.7070698
#> 24 1.018892 -1.1558674 1.8640931
#> 25 1.018892 0.8543368 -0.7070698
#> 26 1.018892 0.8543368 -0.7070698
#> 27 -0.969190 0.8543368 1.8640931
#> 28 -0.969190 0.8543368 1.8640931
#> 29 -0.969190 0.8543368 -0.7070698
#> 30 1.018892 -1.1558674 -0.7070698
#> 31 -0.969190 0.8543368 0.5785117
#> 32 -0.969190 -1.1558674 -0.7070698
#> 33 -0.969190 0.8543368 -0.7070698
#> 34 -0.969190 0.8543368 0.5785117
#> 35 -0.969190 0.8543368 1.8640931
#> 36 -0.969190 0.8543368 -0.7070698
#> 37 1.018892 -1.1558674 0.5785117
#> 38 1.018892 0.8543368 0.5785117
#> 39 1.018892 -1.1558674 -0.7070698
#> 40 1.018892 -1.1558674 0.5785117
#> 41 1.018892 0.8543368 -0.7070698
#> 42 -0.969190 -1.1558674 1.8640931
#> 43 1.018892 0.8543368 -0.7070698
#> 44 1.018892 0.8543368 -0.7070698
#> 45 1.018892 0.8543368 -0.7070698
#> 46 1.018892 -1.1558674 -0.7070698
#> 47 1.018892 -1.1558674 -0.7070698
#> 48 1.018892 -1.1558674 -0.7070698
#> 49 1.018892 -1.1558674 0.5785117
#> 50 -0.969190 -1.1558674 -0.7070698
#> 51 -0.969190 0.8543368 -0.7070698
#> 52 -0.969190 -1.1558674 1.8640931
#> 53 -0.969190 0.8543368 0.5785117
#> 54 -0.969190 -1.1558674 -0.7070698
#> 55 -0.969190 -1.1558674 -0.7070698
#> 56 -0.969190 0.8543368 -0.7070698
#> 57 -0.969190 0.8543368 -0.7070698
#> 58 -0.969190 -1.1558674 -0.7070698
#> 59 1.018892 0.8543368 -0.7070698
#> 60 -0.969190 0.8543368 -0.7070698
#> 61 -0.969190 -1.1558674 0.5785117
#> 62 -0.969190 0.8543368 0.5785117
#> 63 -0.969190 -1.1558674 1.8640931
#> 64 1.018892 0.8543368 -0.7070698
#> 65 -0.969190 -1.1558674 0.5785117
#> 66 -0.969190 0.8543368 -0.7070698
#> 67 1.018892 0.8543368 -0.7070698
#> 68 1.018892 -1.1558674 1.8640931
#> 69 -0.969190 -1.1558674 -0.7070698
#> 70 -0.969190 0.8543368 0.5785117
#> 71 1.018892 -1.1558674 -0.7070698
#> 72 -0.969190 0.8543368 0.5785117
#> 73 -0.969190 0.8543368 0.5785117
#> 74 1.018892 0.8543368 -0.7070698
#> 75 -0.969190 0.8543368 0.5785117
#> 76 1.018892 -1.1558674 1.8640931
#> 77 -0.969190 0.8543368 -0.7070698
#> 78 -0.969190 0.8543368 -0.7070698
#> 79 -0.969190 0.8543368 -0.7070698
#> 80 1.018892 0.8543368 -0.7070698
#>
#> $cox_larsDR
#> Call:
#> coxph(formula = YCsurv ~ ., data = X_larsDR)
#>
#> coef exp(coef) se(coef) z p
#> X_train_microD20S107 0.6263 1.8706 0.4316 1.451 0.146794
#> X_train_microD3S1282 -0.2857 0.7515 0.4547 -0.628 0.529826
#> X_train_microD15S127 0.9521 2.5911 0.3834 2.483 0.013027
#> X_train_microD2S138 -1.3850 0.2503 0.4197 -3.300 0.000967
#> X_train_microD16S408 -0.7357 0.4792 0.3880 -1.896 0.057930
#> X_train_microN 0.4274 1.5332 0.2675 1.598 0.110118
#>
#> Likelihood ratio test=31.8 on 6 df, p=1.784e-05
#> n= 80, number of events= 17
#>
rm(X_train_micro,Y_train_micro,C_train_micro,cox_larsDR_fit)