sPLANN {survivalPLANN}R Documentation

Survival Neural Network by Using the PLANN Method

Description

This function allows to compute a neural network using the PLANN method.

Usage

sPLANN(formula, data, pro.time=NULL, inter, size = 32, decay = 0.01,
                          maxit = 100, MaxNWts = 10000, trace = FALSE, ...)

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 in which to look for the variables included in the formula.

pro.time

A numeric value with the maximum prognostic. If NULL, the maximum prognostic time is the last time for wich an event is observed.

inter

A numeric value representing the length of the intervals.

size

A numeric value for the number of units in the hidden layer. Default is set to 32

decay

A numeric value for the parameter for weight decay. Default is set to 0.01

maxit

A numeric value for the maximum number of iterations. Default is set to 100.

MaxNWts

The maximum allowable number of weights. There is no intrinsic limit in the code, but increasing MaxNWts will probably allow fits that are very slow and time-consuming. Default is set to 10000

trace

A logical argument for tracing optimization. Default is set to FALSE.

...

Further arguments passed to or from other methods.

Details

The nnet loss function parameter is fixed to entropy as Biganzoli's PLANN method minimize the cross-entrophy loss function which correspondS to maximizing the loglikelihood.

Value

formula

The formula object.

fitsurvivalnet

The fitted nnet object.

data

The data frame given with the computed Intervals column added.

data_dup

The base data frame with the duplicated rows based on the Intervals column.

call

The function call for the creation of the nnet object.

inter

The interval length.

size

The number of units in the hidden layer.

decay

The value of the parameter for weight decay.

maxit

The value of the parameter for maxit.

MaxNWts

The value of the parameter for MaxNWts.

coefnames

The name(s) of the covariate(s) used in the model.

y

The values of time and event.

x

The model covariates values.

intervals

The intervals limits values.

missing

The rows with a missing value in the data base.

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(dataK)

splann <- sPLANN(Surv(time, event) ~ sex + stade + delay, data=dataK, inter=365, 
                          size=32, decay=0.01, maxit=100, MaxNWts=10000)

print(splann)

[Package survivalPLANN version 0.4 Index]