
Fitting a PLSR model on the (Deviance) Residuals
Source:R/coxpls2DR.R
, R/coxpls2DR.default.R
, R/coxpls2DR.formula.R
coxpls2DR.Rd
This function computes the PLSR 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. It uses the package pls
.
Usage
coxpls2DR(Xplan, ...)
# Default S3 method
coxpls2DR(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = TRUE,
scaleY = TRUE,
ncomp = min(7, ncol(Xplan)),
methodpls = "kernelpls",
validation = "CV",
plot = FALSE,
allres = FALSE,
...
)
# S3 method for class 'formula'
coxpls2DR(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = TRUE,
scaleY = TRUE,
ncomp = min(7, ncol(Xplan)),
methodpls = "kernelpls",
validation = "CV",
plot = FALSE,
allres = FALSE,
dataXplan = NULL,
subset,
weights,
model_frame = FALSE,
model_matrix = FALSE,
contrasts.arg = NULL,
...
)
Arguments
- Xplan
a formula or a matrix with the eXplanatory variables (training) dataset
- ...
Arguments to be passed on to
survival::coxph
.- 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 thetime2
argument 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
TRUE
and the model was fit with case weights, then the weighted residuals are returned.- scaleX
Should the
Xplan
columns be standardized ?- scaleY
Should the
time
values be standardized ?- ncomp
The number of components to include in the model. The number of components to fit is specified with the argument ncomp. It this is not supplied, the maximal number of components is used (taking account of any cross-validation).
- methodpls
The multivariate regression method to be used. See
mvrCv
for details.- validation
character. What kind of (internal) validation to use. If
validation = "CV"
, cross-validation is performed. The number and type of cross-validation segments are specified with the argumentssegments
andsegment.type
. SeemvrCv
for details. Ifvalidation = "LOO"
, leave-one-out cross-validation is performed. It is an error to specify the segments whenvalidation = "LOO"
is specified.- plot
Should the survival function be plotted ?)
- allres
FALSE to return only the Cox model and TRUE for additionnal results. See details. Defaults to FALSE.
- dataXplan
an optional data frame, list or environment (or object coercible by
as.data.frame
to a data frame) containing the variables in the model. If not found indataXplan
, the variables are taken fromenvironment(Xplan)
, typically the environment from whichcoxpls2DR
is 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
NULL
or 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_pls2DR
Final Cox-model.
If
allres=TRUE
:
- tt_pls2DR
PLSR components.
- cox_pls2DR
Final Cox-model.
- pls2DR_mod
The PLSR model.
Details
If allres=FALSE
returns only the final Cox-model. If
allres=TRUE
returns a list with the PLS components, the final
Cox-model and the PLSR 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_pls2DR_fit=coxpls2DR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="none"))
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls2DR)
#>
#> coef exp(coef) se(coef) z p
#> Comp.1 0.7784 2.1781 0.1987 3.917 8.96e-05
#> Comp.2 0.9626 2.6186 0.2982 3.228 0.00125
#> Comp.3 0.9110 2.4868 0.4075 2.236 0.02536
#> Comp.4 0.9022 2.4650 0.4004 2.253 0.02424
#> Comp.5 0.1844 1.2026 0.2664 0.692 0.48865
#> Comp.6 0.7448 2.1059 0.4228 1.761 0.07819
#>
#> Likelihood ratio test=54.95 on 6 df, p=4.745e-10
#> n= 80, number of events= 17
(cox_pls2DR_fit2=coxpls2DR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,validation="none"))
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls2DR)
#>
#> coef exp(coef) se(coef) z p
#> Comp.1 0.7784 2.1781 0.1987 3.917 8.96e-05
#> Comp.2 0.9626 2.6186 0.2982 3.228 0.00125
#> Comp.3 0.9110 2.4868 0.4075 2.236 0.02536
#> Comp.4 0.9022 2.4650 0.4004 2.253 0.02424
#> Comp.5 0.1844 1.2026 0.2664 0.692 0.48865
#> Comp.6 0.7448 2.1059 0.4228 1.761 0.07819
#>
#> Likelihood ratio test=54.95 on 6 df, p=4.745e-10
#> n= 80, number of events= 17
(cox_pls2DR_fit3=coxpls2DR(~.,Y_train_micro,C_train_micro,ncomp=6,validation="none",
dataXplan=X_train_micro_df))
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls2DR)
#>
#> coef exp(coef) se(coef) z p
#> Comp.1 0.7784 2.1781 0.1987 3.917 8.96e-05
#> Comp.2 0.9626 2.6186 0.2982 3.228 0.00125
#> Comp.3 0.9110 2.4868 0.4075 2.236 0.02536
#> Comp.4 0.9022 2.4650 0.4004 2.253 0.02424
#> Comp.5 0.1844 1.2026 0.2664 0.692 0.48865
#> Comp.6 0.7448 2.1059 0.4228 1.761 0.07819
#>
#> Likelihood ratio test=54.95 on 6 df, p=4.745e-10
#> n= 80, number of events= 17
rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls2DR_fit,cox_pls2DR_fit2,cox_pls2DR_fit3)