RCASPAR (version 1.18.0)

weights_xvBLH: A special version of STpredictor.BLH used within k-xv to predict the survival times of the kth validation group in the cross validation step.

Description

This function is an incomplete version of STpredictor.BLH used within the cross validation function STpredictor_xvBLH to predicted the survival times of the subset of patients in the kth partitioning. It is not meant for use outside that function.

Usage

weights_xvBLH(geDataS, survDataS, geDataT, survDataT, q = 1, s = 1, a = 2, b = 2, groups = 3, par, method = "BFGS", noprior = 1, extras = list())

Arguments

geDataS

The co-variate data of the kth validation set passed on by STpredictor.xv.BLH. It is a matrix with the co-variates in the columns and the subjects in the rows. Each cell corresponds to that rowth subject's columnth co-variate's value.

survDataS

The survival data of the kth validation set passed on by STpredictor_xvBLH. It takes on the form of a data frame with at least have the following columns True_STs and censored, corresponding to the observed survival times and the censoring status of the subjects consecutively. Censored patients are assigned a 1 while patients who experience an event are assigned 1.

geDataT

The co-variate data of the kth training set passed on by STpredictor_xvBLH.

survDataT

The survival data of the kth training set passed on by STpredictor_xvBLH.

q

One of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.

s

The second of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.

a

The shape parameter for the gamma distribution used as a prior on the baseline hazards.

b

The scale parameter for the gamma distribution used as a prior on the baseline hazards.

groups

The number of partitions along the time axis for which a different baseline hazard is to be assigned. This number should be the same as the number of initial values passed for the baseline hazards in the beginning of the weights_baselineH argument.

par

A single vector with the initial values of the baseline hazards followed by the weights(regression coefficients) for the co-variates.

method

The preferred optimization method. It can be one of the following: "Nelder-Mead": for the Nelder-Mead simplex algorithm. "L-BFGS-B" for the L-BFGS-B quasi-Newtonian method. "BFGS" for the BFGS quasi-Newtonian method. "CG" for the Conjugate Gradient decent method. "SANN": for the simulated annealing algorithm.

noprior

An integer indicating the number of iterations to be done without assuming a prior on the regression coefficients.

extras

The extra arguments to passed to the optimization function optim. For further details on them, see the documentation for the optim function.

Value

• prediction
  A data frame with the columns True_STs (the observed survival times), Predicted_STs (the predicted survival times), censored(the censoring status of the patient,absolute_error(the sign-less difference between the predicted and observed survival times), PatientOrderValidation (The patient's number)
• est.geneweight
  The estimated regression coefficients from the kth training set (geDataT,survDataT)
• est.baselineH
  The estimated baseline hazards from the kth training set (geDataT, survDataT)

See Also

STpredictor_BLH

Examples

data(Bergamaschi)
data(survData)
weights_xvBLH(geDataS=Bergamaschi[21:31, 1:2], survDataS=survData[21:31, 9:10],geDataT=Bergamaschi[1:20, 1:2], 
survDataT=survData[1:20, 9:10], q = 1, s = 1, a = 2, b = 2, groups = 3, par = c(0.1, 0.1, 0.1,rep(0,2)), 
method = "CG", noprior = 1, extras = list(reltol=1))