glmBfp v0.0-51


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Bayesian Fractional Polynomials for GLMs

Implements the Bayesian paradigm for fractional polynomials in generalized linear models, described by Held, Gravestock, Sabanes Bove (2015) <doi:10.1214/14-STS510>. See package 'bfp' for the treatment of normal models.

Functions in glmBfp

Name Description
CustomGPrior-class The custom g-prior class
CustomGPrior Constructor for the custom g-prior class
Extract.GlmBayesMfp Extract method for GlmBayesMfp objects
GPrior-class The virtual g-prior class
HypergPrior-class The hyper-g prior class
HypergPrior-initialize Initialization method for the "HypergPrior" class
HypergPrior Constructor for the hyper-g prior class
IncInvGammaGPrior-class The incomplete inverse gamma g-prior class
GlmBayesMfpSamples-class Class for samples from a single GlmBayesMfp model or a model average
GlmBayesMfpSamples-subsetting Subset method for GlmBayesMfpSamples objects
McmcOptions Constructor for class McmcOptions Convert a GlmBayesMfp object into a data frame
coxTBF Fit Cox models using glmBayesMfp
InvGammaGPrior-class The inverse gamma g-prior class
InvGammaGPrior-initialize Initialization method for the "InvGammaGPrior" class
constructNewdataMatrix Construct the covariates matrix for new data based on an existing GlmBayesMfp object
InvGammaGPrior Constructor for the inverse gamma g-prior class
McmcOptions-class Class for the three canonical MCMC options
cppOptimize Interface to the internal C++ optimization routine "optimize"
empiricalHpd Construct an empirical HPD interval from samples
cppBfgs Interface to the internal C++ optimization routine "bfgs"
getLogGPrior Helper function for glmBayesMfp: Returns the normalized log g prior density
getLogMargLikEstimate Compute the Chib-Jeliazkov log marginal likelihood estimate from the MCMC output
logPriors Extract the log prior values from a GlmBayesMfp object
convert2Mcmc Convert samples to mcmc objects
boxTidwell Box Tidwell transformation
computeModels Compute model information for a given list of model configurations and glmBayesMfp output.
fpScale Shift and scale a covariate vector (if wished) to have positive and small numbers.
getDesignMatrix Construct the design matrix for a given bfp GLM model
getFamily Helper function for glmBayesMfp: Extracts an S3 family object
glmBfp-package Bayesian inference for fractional polynomial models from the GLM and Cox family
inclusionProbs Compute posterior inclusion probabilites based on GlmBayesMfp object
fpTrans Transform formula variables
getFpTransforms Get the FP transforms matrix of a given covariate vector
getGenerator Internal helper function which gets the generator (and normalizing constant)
posteriors Extract posterior model probability estimates from a GlmBayesMfp object
getUncenteredDesignMatrix Construct the design matrix for a given bfp GLM model
glmBayesMfp Bayesian model inference for fractional polynomial GLMs and Cox models
predict.TBFcox Prediction methods for CoxTBF objects
predict.TBFcox.sep Prediction methods for CoxTBF objects with separate estimates
sampleGlm Produce posterior samples from one GLM / Cox model
sampleSize Compute the number of samples for a given MCMC options triple
writeFormula Construct a survival formula based on a glmBfp object with censInd not null.
IncInvGammaGPrior-initialize Initialization method for the "IncInvGammaGPrior" class
IncInvGammaGPrior Constructor for the incomplete inverse gamma g-prior class
evalZdensity Evaluate the (negative log) unnormalized marginal z density in a given model.
bfp Mark a covariate for transformation with fractional polynomials
getMarginalZ Construct a (smooth) marginal z density approximation from a model information list
getModelCoefs Estimate shrunken coefficients from GlmBayesMfp object for Cox model
is.bool Predicate checking for a boolean option
logMargLiks Extract the log marginal likelihood estimates from a GlmBayesMfp object
scrHpd Calculate an SCB from a samples matrix
testCox Test the Cox model computation for the TBF approach
plotCurveEstimate Function for plotting a fractional polynomial curve estimate
print.GlmBayesMfp Print a GlmBayesMfp object.
sampleBma Produce posterior samples from a Bayesian model average over GLMs / Cox models
predict.TBFcox.BMA Prediction methods for CoxTBF objects for BMA models
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License GPL (>= 2)
LinkingTo Rcpp, RcppArmadillo
LazyLoad yes
Date 2017-07-31
Encoding UTF-8
Collate 'GPrior-classes.R' 'posteriors.R' 'GlmBayesMfp-methods.R' 'GlmBayesMfpSamples-class.R' 'GlmBayesMfpSamples-methods.R' 'McmcOptions-class.R' 'McmcOptions-methods.R' 'RcppExports.R' 'helpers.R' 'computeModels.R' 'constructNewdataMatrix.R' 'getFpTransforms.R' 'getDesignMatrix.R' 'coxTBF.R' 'evalZdensity.R' 'formula.R' 'fpScale.R' 'fpTrans.R' 'getFamily.R' 'getLogGPrior.R' 'getLogMargLikEstimate.R' 'getMarginalZ.R' 'sampleGlm.R' 'sampleBma.R' 'getModelCoefs.R' 'glmBayesMfp.R' 'glmBfp-package.R' 'hpds.R' 'inclusionProbs.R' 'optimize.R' 'plotCurveEstimate.R' 'predictCoxTBF.R' 'testCox.R' 'uncenteredDesignMatrix.R' 'writeFormula.R'
RoxygenNote 5.0.1
Repository CRAN
Repository/R-Forge/Project bfp
Repository/R-Forge/Revision 144
Repository/R-Forge/DateTimeStamp 2017-08-03 06:51:05
Date/Publication 2017-08-03 09:55:53 UTC
NeedsCompilation yes
Packaged 2017-08-03 07:13:36 UTC; rforge

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