cvPLANN {survivalPLANN} | R Documentation |
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)
Arguments
formula |
The |
pro.time |
The prognostic time at which the metric is evaluated. If |
data |
A data frame in which to look for the variables included in the |
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 |
Value
optimal |
A list giving the optimal value for each parameter. |
results |
A data frame listing the parameters combinaison and their metrics values. |
See Also
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