predict.fpen {frailtyMMpen}

Estimate the baseline hazard or the predict hazard rate based on the new data for penalized regression

Description

This function is used to estimate the baseline hazard or to predict the hazard rate of a specific individual given result from model fitting.

Usage

##S3 method for class "fpen"

Arguments

object

Object with class "fpen"

tune

The tuning parameter for estimating coefficients

coef

Instead of providing tuning parameter, you can directly provide the coefficients for prediction

newdata

The new data for prediction of hazard, categorical data has to be transformed to 0 and 1

surv

Plot survival curve instead of cumulative hazard, the default is FALSE

...

Further arguments pass to or from other methods

Details

If parameter newdata is given, the predicted hazard is calculated based on the given data. If parameter newdata is not given, the estimation of baseline hazard will be returned. Since the covariance of estimated parameters for penalized regression cannot be obtained from MLE theorem, we only provide the estimation without confidence band. For the formulation of new data, you may refer to function predict.fmm for detailed description.

Value

output

A dataframe that the first column is the evaluated time point and the second column is the estimated cumulative hazard or survival curve.

Examples

 
data(simdataCL)


gam_cl = frailtyMMpen(Surv(time, status) ~  . + cluster(id), simdataCL, frailty = "Gamma")

# Calculate the survival curve based on baseline hazard
predict(gam_cl, surv = TRUE)

# Construct new data and calculate the cumulative hazard based on new data
newcl = c(gam_cl$X[1,], 2)
names(newcl) = c(gam_cl$coefname, "id")
predict(gam_cl, newdata = newcl)