weights_xvBLH: A special version of STpredictor.BLH used within k-xv to...

In RCASPAR: A package for survival time prediction based on a piecewise baseline hazard Cox regression model.

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)

Note

This function is not meant to be used outside its wrapper.

Author(s)

Douaa Mugahid

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))

RCASPAR documentation built on Nov. 8, 2020, 6:56 p.m.