survivalPLANN (version 0.4)

cvPLANN: Cross-validation method for a Neural Netwotk Model with the PLANN Method.

Description

Cross-validation method for the hyper-parameters for an object of class sPLANN.

Usage

cvPLANN(formula, pro.time=NULL, data, cv=10, inter=1, size=8, decay=0.01,
                    maxit=1000, MaxNWts=10000)

Value

optimal

A list giving the optimal value for each parameter.

results

A data frame listing the parameters combinaison and their metrics values.

Arguments

formula

The formula object.

pro.time

The prognostic time at which the metric is evaluated. If NULL, the median of the times is chosen.

data

A data frame in which to look for the variables included in the formula.

cv

The number of splits for cross-validation. The default value is 10.

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 8

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 1000.

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

See Also

sPLANN

Examples

data(dataK) # the database with the observed sample

tune.sPLANN <- cvPLANN(Surv(time, event)~ stade + delay, data=dataK, cv=3,
                     inter=365.241, size=c(2, 4), decay=c(0.01))
                     
tune.sPLANN$optimal