survivalSL (version 0.98)

LIB_PLANN: Library of the Super Learner for Survival Neural Network Based on the PLANN Method

Description

Fit a neural network based on the partial logistic regression.

Usage

LIB_PLANN(formula, data, inter, size, decay,
          maxit, MaxNWts, maxtime=NULL)

Value

formula

The formula object used for model construction.

model

The estimated model.

data

The data frame used for learning.

times

A vector of numeric values with the times of the predictions.

predictions

A matrix with the predictions of survivals of each subject (lines) for each observed time (columns).

Arguments

formula

A formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.

data

A data frame whose columns correspond to the variables present in the formula.

inter

The length of the intervals.

size

The number of units in the hidden layer.

decay

The parameter for weight decay.

maxit

The maximum number of iterations.

MaxNWts

The maximum allowable number of weights.

maxtime

A numeric value with the maximum prognostic time. If NULL, the maximum prognostic time is the maximum value of database times + 1.

Details

This function is based is based on the survivalPLANN from the related package.

References

Biganzoli E, Boracchi P, Mariani L, and et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med, 17:1169-86, 1998.

Examples

data("dataDIVAT2")

# The neural network based from the first 300 individuals of the data base

formula<-Surv(times,failures) ~ age + hla + retransplant + ecd
model <- LIB_PLANN(formula, data=dataDIVAT2[1:300,],
  inter=0.5, size=32, decay=0.01, maxit=100, MaxNWts=10000, maxtime=NULL)

# The predicted survival of the first subject of the training sample

plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))