survivalPLANN {survivalPLANN} | R Documentation |
This function allows to compute a neural network using the PLANN method.
survivalPLANN(formula, data, inter, size = 32, decay = 0.01,
maxit = 100, MaxNWts = 10000, trace = FALSE, ...)
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 |
data |
A data frame in which to look for the variables included in the |
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. |
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.
formula |
The |
fitsurvivalnet |
The fitted |
data |
The data frame given with the computed |
data_dup |
The base data frame with the duplicated rows based on the |
call |
The function call for the creation of the |
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 |
x |
The model covariates values. |
intervals |
The intervals limits values. |
missing |
The rows with a missing value in the data base. |
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.
data(dataK)
splann <- survivalPLANN(Surv(time, event) ~ sex + stade + delay, data=dataK, inter=365,
size=32, decay=0.01, maxit=100, MaxNWts=10000)
print(splann)