Fitting a Cox-Model on PLSR components

This function computes the the Cox-Model with PLSR components as the explanatory variables. It uses the package plsRglm.

coxpls3(Xplan, ...)

# S3 method for default
coxpls3(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  ...
)

# S3 method for formula
coxpls3(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  ...
)

Arguments

Xplan	a formula or a matrix with the eXplanatory variables (training) dataset
...	Arguments to be passed on to `survival::coxph` and to `plsRglm::PLS_lm`.
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 at `time`, 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 the `time2` 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 ?
nt	Number of PLSR components to fit.
typeVC	type of leave one out crossed validation. Several procedures are available and may be forced. list("none") no crossed validation list("standard") as in SIMCA for datasets without missing values and with all values predicted as those with missing values for datasets with any missing values list("missingdata") all values predicted as those with missing values for datasets with any missing values list("adaptative") predict a response value for an x with any missing value as those with missing values and for an x without any missing value as those without missing values.
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.
sparse	should the coefficients of non-significant predictors (<`alpha.pvals.expli`) be set to 0
sparseStop	should component extraction stop when no significant predictors (<`alpha.pvals.expli`) are found
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 in `dataXplan`, the variables are taken from `environment(Xplan)`, typically the environment from which `coxpls3` 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.

Value

If allres=FALSE :

cox_pls3

Final Cox-model.

If allres=TRUE :

tt_pls3

PLSR components.

cox_pls3

Final Cox-model.

pls3_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@math.unistra.fr
http://www-irma.u-strasbg.fr/~fbertran/

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_pls3_fit <- coxpls3(X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Predicting X without NA neither in X nor in Y____
#> Le chargement a nécessité le package : plsdof
#> ****________________________________________________****
#> 
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls3)
#> 
#>             coef exp(coef)  se(coef)      z      p
#> Comp_1 -0.576658  0.561773  0.227463 -2.535 0.0112
#> Comp_2 -0.634002  0.530465  0.283716 -2.235 0.0254
#> Comp_3 -0.565765  0.567926  0.249533 -2.267 0.0234
#> Comp_4  0.290360  1.336909  0.290257  1.000 0.3171
#> Comp_5 -0.378710  0.684744  0.231913 -1.633 0.1025
#> Comp_6  0.238997  1.269975  0.281797  0.848 0.3964
#> Comp_7  0.005897  1.005915  0.282417  0.021 0.9833
#> 
#> Likelihood ratio test=26.12  on 7 df, p=0.0004802
#> n= 80, number of events= 17 
(cox_pls3_fit2 <- coxpls3(~X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
#> Warning: non-list contrasts argument ignored
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls3)
#> 
#>             coef exp(coef)  se(coef)      z      p
#> Comp_1 -0.576658  0.561773  0.227463 -2.535 0.0112
#> Comp_2 -0.634002  0.530465  0.283716 -2.235 0.0254
#> Comp_3 -0.565765  0.567926  0.249533 -2.267 0.0234
#> Comp_4  0.290360  1.336909  0.290257  1.000 0.3171
#> Comp_5 -0.378710  0.684744  0.231913 -1.633 0.1025
#> Comp_6  0.238997  1.269975  0.281797  0.848 0.3964
#> Comp_7  0.005897  1.005915  0.282417  0.021 0.9833
#> 
#> Likelihood ratio test=26.12  on 7 df, p=0.0004802
#> n= 80, number of events= 17 
(cox_pls3_fit3 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",data=X_train_micro_df))
#> Warning: non-list contrasts argument ignored
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls3)
#> 
#>             coef exp(coef)  se(coef)      z      p
#> Comp_1 -0.576658  0.561773  0.227463 -2.535 0.0112
#> Comp_2 -0.634002  0.530465  0.283716 -2.235 0.0254
#> Comp_3 -0.565765  0.567926  0.249533 -2.267 0.0234
#> Comp_4  0.290360  1.336909  0.290257  1.000 0.3171
#> Comp_5 -0.378710  0.684744  0.231913 -1.633 0.1025
#> Comp_6  0.238997  1.269975  0.281797  0.848 0.3964
#> Comp_7  0.005897  1.005915  0.282417  0.021 0.9833
#> 
#> Likelihood ratio test=26.12  on 7 df, p=0.0004802
#> n= 80, number of events= 17 
(cox_pls3_fit4 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE))
#> Warning: non-list contrasts argument ignored
#> ____************************************************____
#> No significant predictors (<0.05) found
#> Warning only one standard component (without sparse option) was thus extracted
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls3)
#> 
#>           coef exp(coef) se(coef)      z      p
#> Comp_1 -0.4653    0.6279   0.1719 -2.707 0.0068
#> 
#> Likelihood ratio test=7.04  on 1 df, p=0.00798
#> n= 80, number of events= 17 
(cox_pls3_fit5 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=FALSE,sparseStop=TRUE))
#> Warning: non-list contrasts argument ignored
#> ____************************************************____
#> No significant predictors (<0.05) found
#> Warning only one standard component (without sparse option) was thus extracted
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls3)
#> 
#>           coef exp(coef) se(coef)      z      p
#> Comp_1 -0.4653    0.6279   0.1719 -2.707 0.0068
#> 
#> Likelihood ratio test=7.04  on 1 df, p=0.00798
#> n= 80, number of events= 17 

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3_fit,cox_pls3_fit2,
cox_pls3_fit3,cox_pls3_fit4,cox_pls3_fit5)