pact.cv: Cross-validation for pact

In pact: Predictive Analysis of Clinical Trials

Description

Predictive scores using k-fold cross-validation for the model developed in pact.fit

Usage

pact.cv(p, nfold)

Arguments

p	An object of class 'pact'
nfold	The number of folds (k) for the k-fold cross-validation. k equal to the sample size would mean a leave-one-out cross-validation

Details

Obtain cross-validated predictive scores for the model developed in pact.fit. In each fold of the cross-validation, a model is developed from the observations in the training set using the same variable selection parameters as that used for the model developed in pact.fit. The estimated coefficients of the regression model developed using training set are used to make predictions for the left out observations (test set). This is repeated for all the folds. Scores are thus obtained for all the subjects in the dataset. The function eval.pact.cv provides various evaluation options for the cross-validated scores.

Value

A list with the following components

PredScore	The cross-validated scores for each subject (a vector)
Y	The response variable used
Xf	The dataframe of fixed prognostic covariates
Xv	The dataframe of candidate predictive variables
Treatment	The treatment assignment indicator used
nCovarf	The number of variables in Xf
nCovarv	The number of variables in Xv
family	Type of the response variable
varSelect	The variable selection method used
nsig, cvfolds.varSelect, which.lambda, penalty.scaling	The variable selection parameters used
call	The call that produced this output

Author(s)

Jyothi Subramanian and Richard Simon
Maintainer: Jyothi Subramanian <[e-mail]>

Examples

data(prostateCancer)
Y <- prostateCancer[,3:4]
Xf <- prostateCancer[,7:8]
Xv <- prostateCancer[,c(5:6,9)]
Treatment <- as.factor(prostateCancer[,2])
p <- pact.fit(Y=Y,Xf=Xf,Xv=Xv,Treatment=Treatment,family="cox",varSelect="lasso")
cv <- pact.cv(p, nfold=5)

pact documentation built on May 2, 2019, 1:48 a.m.