bayesmc: Bayesian method for high-dimensional variable selection
In icensmis: Study Design and Data Analysis in the Presence of Error-Prone Diagnostic Tests and Self-Reported Outcomes
Description
Bayesian method for high-dimensional variable selection
Usage
1
Arguments
Dm
the D matrix
Xmat
the design matrix
b
the prior distribution parameter for beta, normal std
om1
the prior distribution parameter for omega
om2
the piror distribution parameter for omega
niter
number of iteration
psample
the sampling probability for updading regresson coefficient
initsurv
initial survival probabilities at end of study
nreport
every how many iterations to output parameters
fitsurv
the survival parameters optimization function
icensmis documentation built on Sept. 5, 2021, 5:49 p.m.
R Package Documentation
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Description
Bayesian method for high-dimensional variable selection
Usage
1 |
Arguments
Dm |
the D matrix |
Xmat |
the design matrix |
b |
the prior distribution parameter for beta, normal std |
om1 |
the prior distribution parameter for omega |
om2 |
the piror distribution parameter for omega |
niter |
number of iteration |
psample |
the sampling probability for updading regresson coefficient |
initsurv |
initial survival probabilities at end of study |
nreport |
every how many iterations to output parameters |
fitsurv |
the survival parameters optimization function |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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