Description Usage Arguments Value Note Author(s) References Examples
This function is a wrapper around the optimization function optim
to allow the optimization for the regression coefficients and baseline
hazards appropriate for the data set at hand. It is where the functions weight_estimator_BLH, weight_estimator_BLH_noprior,
deriv_weight_estimator_BLH, deriv_weight_estimator_BLH_noprior
are required.
1 2 
geDataT 
A matrix with the covariate in the columns and the subjects in the rows.Each cell corresponds to that rowth subject's columnth covariate's value. 
survDataT 
A data frame with the survival data of the set of subjects at hand. It should 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”. 
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 covariates. 
method 
The preferred optimization method. It can be one of the following:

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 
dist 
The distribution function to be passed to the optimization algorithm in case of using SANN to generate a new candidate point. 
The same value as the optim
function. See it's documentation for details.
Note that this function is just a wrapper around the optim
function to serve our purpose, and it's main purpose is to be called within the main functions of this package
STpredictor_BLH
and weights_xvBLH
Douaa Mugahid
http://sekhon.berkeley.edu/stats/html/optim.html
1 2 3 4  data(Bergamaschi)
data(survData)
weights_BLH(geDataT=Bergamaschi[1:10,1:2], survDataT=survData[1:10, 9:10], q=1, s=1, a=1.56, b=0.17, groups=3, par=c(0.1,0.2,0.3,rep(0,ncol(Bergamaschi))), method = "CG", noprior = 1, extras =
list(reltol=1), dist = NULL)

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