setHyperparams: Definition of characteristics of sample datasets for profile regression

Description

Hyperparameters for the priors can be specified here and passed as an argument to profRegr.

The user can specify some or all hyperparameters. Those hyperparameters not specified will take their default values. Where the file is not provided, all hyperparameters will take their default values.

Usage

setHyperparams(shapeAlpha=NULL,rateAlpha=NULL,
     aPhi=NULL,mu0=NULL,Tau0=NULL,R0=NULL,
     kapp0=NULL,muTheta=NULL,sigmaTheta=NULL,dofTheta=NULL,muBeta=NULL,
     sigmaBeta=NULL,dofBeta=NULL,shapeTauEpsilon=NULL,
     rateTauEpsilon=NULL,aRho=NULL,bRho=NULL,atomRho=NULL,shapeSigmaSqY=NULL,
     scaleSigmaSqY=NULL,rSlice=NULL,truncationEps=NULL)

Arguments

shapeAlpha

The shape parameter for Gamma prior on alpha (default=2)

rateAlpha

The inverse-scale (rate) parameter for the Gamma prior on alpha (default=1)

aPhi

The vector of parameters for the Dirichlet prior on phi_j. Element j corresponds to covariate j which then has a prior Dirichlet(aPhi[j],aPhi[j],....,aPhi[j]). Only used in discrete case, default=(1 1 1 ... 1).

mu0

The mean vector for mu_c in the Normal covariate case (only used in Normal covariate case, default=empirical covariate means)

Tau0

The precision matrix for mu_c in the Normal covariate case (only used in Normal covariate case, default=inverse of diagonal matrix with elements equal to squareof empirical range for each covariate)

The matrix parameter for the Wishart distribution for Tau_c (only used in Normal covariate case, default=1/nCovariates * inverse of empirical covariance matrix)

kapp0

The degrees of freedom parameter for the Wishart distribution for Tau_c (only used in Normal covariate case, default=nCovariates).

muTheta

The location parameter for the t-Distribution for theta_c (only used if response included in model, default=0)

sigmaTheta

The scale parameter for the t-Distribution for theta_c (only used if response included in model, default=2.5)

dofTheta

The degrees of freedom parameter for the t-Distribution for theta_c (only used if response included in model, default=7)

muBeta

The location parameter for the t-Distribution for beta (only used when fixed effects present, default=0)

sigmaBeta

The scale parameter for the t-Distribution for beta (only used when fixed effects present, default=2.5)

dofBeta

The dof parameter for the t-Distribution for beta (only used when fixed effects present, default=7)

shapeTauEpsilon

Shape parameter for gamma distribution for prior for precision tau of extra variation errors epsilon (only used if extra variation is used i.e. extraYVar argument is included, default=5.0)

rateTauEpsilon

Inverse-scale (rate) parameter for gamma distribution for prior for precision tau of extra variation errors epsilon (only used if extra variation is used i.e. extraYVar argument is used, default=0.5)

aRho

Parameter for beta distribution for prior on rho in variable selection (default=0.5)

bRho

Parameter for beta distribution for prior on rho in variable selection (default=0.5)

atomRho

Parameter for the probability for the atom at zero, i.e. the 0.5 probability in w_j distributed Bernoulli(0.5) in the formulation of the sparsity inducing prior (default=0.5). This parameter must be in the interval (0,1], where atomRho=1 corresponds to th

shapeSigmaSqY

Shape parameter of inverse-gamma prior for sigma_Y^2 (only used in the Normal response model, default =2.5)

scaleSigmaSqY

Scale parameter of inverse-gamma prior for sigma_Y^2 (only used in the Normal response model, default =2.5)

rSlice

Slice parameter for independent slice sampler such that xi_c = (1-rSlice)*rSlice^c for c=0,1,2,... (only used for slice independent sampler i.e. sampler=SliceIndependent, default 0.75).

truncationEps

Parameter for determining the truncation level of the finite Dirichlet process (only used for truncated sampler i.e. sampler=Truncated

Value

The output of this function is a list with the components defined as above.

Authors

David Hastie, Department of Epidemiology and Biostatistics, Imperial College London, UK

Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK

Maintainer: Silvia Liverani

References

Liverani, S., Hastie, D. I., Azizi, L., Papathomas, M. and Richardson, S. (2013) PReMiuM: An R package for Profile Regression Mixture Models using Dirichlet Processes. Submitted. Available at http://uk.arxiv.org/abs/1303.2836

Examples

Run this code

hyp <- setHyperparams(shapeAlpha=3,rateAlpha=2,mu0=c(30,13),R0=3.2*diag(2))

inputs <- generateSampleDataFile(clusSummaryPoissonNormal())
runInfoObj<-profRegr(yModel=inputs$yModel, 
    xModel=inputs$xModel, nSweeps=2, nClusInit=15,
    nBurn=2, data=inputs$inputData, output="output", 
    covNames = inputs$covNames, outcomeT = inputs$outcomeT,
    fixedEffectsNames = inputs$fixedEffectNames,
    hyper=hyp)

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