pec accepts only R-objects for which a predictSurvProb method exists and glmnet is not such an object.

Currently, predictSurvProb methods are available for the following R-objects:
matrix
aalen, cox.aalen from library(timereg)
mfp from library(mfp)
phnnet, survnnet from library(survnnet)
rpart (from library(rpart))
coxph, survfit from library(survival)
cph, psm from library(rms)
prodlim from library(prodlim)
glm from library(stats)

For calculating the brier score for glmnet one needs to use the peperr package with the c060 library that wraps glmnet as an object suitable for peperr.

peperr_glmnet_noerror <- peperr(response=Surv(time, status), x=x, 
                    fit.fun=fit.glmnet, args.fit=list(family="cox"),
                    complexity=complexity.glmnet,args.complexity=list(family="cox"),
                    indices=resample.indices(n=length(time), method="boot", sample.n=10))

To get the integrated brier score for the entire model it seems one needs to use the ipec function but I still need to research that.

Many thanks to Thomas Hielscher who authored the c060 package and was extremely kind to help me with this.

pec accepts only R-objects for which a predictSurvProb method exists and glmnet is not such an object.

Currently, predictSurvProb methods are available for the following R-objects:
matrix
aalen, cox.aalen from library(timereg)
mfp from library(mfp)
phnnet, survnnet from library(survnnet)
rpart (from library(rpart))
coxph, survfit from library(survival)
cph, psm from library(rms)
prodlim from library(prodlim)
glm from library(stats)

For calculating the brier score for glmnet one needs to use the peperr package with the c060 library that wraps glmnet as an object suitable for peperr.

peperr_glmnet_noerror <- peperr(response=Surv(time, status), x=x, 
                    fit.fun=fit.glmnet, args.fit=list(family="cox"),
                    complexity=complexity.glmnet,args.complexity=list(family="cox"),
                    indices=resample.indices(n=length(time), method="boot", sample.n=10))

To get the integrated brier score for the entire model it seems one needs to use the ipec function but I still need to research that.

Many thanks to Thomas Hielscher who authored the c060 package and was extremely kind to help me with this.

pec accepts only R-objects for which a predictSurvProb method exists and glmnet is not such an object. In theory for calculating the brier score for glmnet one needs to use the peperr package with the c060 library that packages glmnet as an object suitable for peperr. I am afraid that when I tried to implement that I got an error which I am currently trying to resolve.

pec accepts only R-objects for which a predictSurvProb method exists and glmnet is not such an object.

Currently, predictSurvProb methods are available for the following R-objects:
matrix
aalen, cox.aalen from library(timereg)
mfp from library(mfp)
phnnet, survnnet from library(survnnet)
rpart (from library(rpart))
coxph, survfit from library(survival)
cph, psm from library(rms)
prodlim from library(prodlim)
glm from library(stats)

For calculating the brier score for glmnet one needs to use the peperr package with the c060 library that wraps glmnet as an object suitable for peperr.

peperr_glmnet_noerror <- peperr(response=Surv(time, status), x=x, 
                    fit.fun=fit.glmnet, args.fit=list(family="cox"),
                    complexity=complexity.glmnet,args.complexity=list(family="cox"),
                    indices=resample.indices(n=length(time), method="boot", sample.n=10))

To get the integrated brier score for the entire model it seems one needs to use the ipec function but I still need to research that.

Many thanks to Thomas Hielscher who authored the c060 package and was extremely kind to help me with this.