`R/coxpls.R`

, `R/coxpls.default.R`

, `R/coxpls.formula.R`

`coxpls.Rd`

This function computes the Cox Model based on PLSR components computed model with

as explanatory variables: Xplan.

It uses the package `mixOmics`

to perform PLSR
fit.

```
coxpls(Xplan, ...)
# S3 method for default
coxpls(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = TRUE,
scaleY = TRUE,
ncomp = min(7, ncol(Xplan)),
modepls = "regression",
plot = FALSE,
allres = FALSE,
...
)
# S3 method for formula
coxpls(
Xplan,
time,
time2,
event,
type,
origin,
typeres = "deviance",
collapse,
weighted,
scaleX = TRUE,
scaleY = TRUE,
ncomp = min(7, ncol(Xplan)),
modepls = "regression",
plot = FALSE,
allres = FALSE,
dataXplan = NULL,
subset,
weights,
model_frame = FALSE,
model_matrix = FALSE,
contrasts.arg,
...
)
```

- 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 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 ?- ncomp
The number of components to include in the model. It this is not supplied, min(7,maximal number) components is used.

- modepls
character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See

`pls`

for details- 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 in`dataXplan`

, the variables are taken from`environment(Xplan)`

, typically the environment from which`coxpls`

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.

If `allres=FALSE`

:

- cox_pls
Final Cox-model.

If
`allres=TRUE`

:

- tt_pls
PLSR components.

- cox_pls
Final Cox-model.

- pls_mod
The PLSR model.

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.

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.

```
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_pls_fit=coxpls(X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_pls)
#>
#> coef exp(coef) se(coef) z p
#> dim.1 -0.5771 0.5615 0.2266 -2.546 0.0109
#> dim.2 -0.6350 0.5299 0.2798 -2.269 0.0233
#> dim.3 -0.5675 0.5670 0.2358 -2.407 0.0161
#> dim.4 0.2900 1.3365 0.2897 1.001 0.3167
#> dim.5 -0.3797 0.6841 0.2272 -1.671 0.0947
#> dim.6 0.2398 1.2710 0.2790 0.860 0.3899
#>
#> Likelihood ratio test=26.12 on 6 df, p=0.0002119
#> n= 80, number of events= 17
(cox_pls_fit=coxpls(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): argument "contrasts.arg" is missing, with no default
(cox_pls_fit=coxpls(~.,Y_train_micro,C_train_micro,ncomp=6,dataXplan=X_train_micro_df))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): argument "contrasts.arg" is missing, with no default
rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls_fit)
```