CombinePosteriors: Combines posteriors from two models

In markvdwiel/ShrinkBayes: Bayesian analysis of high-dimensional omics data, either Gaussian or counts

Combines posteriors from two models

Description

This function combines posteriors from two models for a given parameter and, possibly, for related linear combinations. The mixture prior probabilities should be present in shrinksimul.

Usage

CombinePosteriors(fullpost1, fullpost2, shrinksimul, para, includelincomb = TRUE, margcomb = c("marginals.fixed"), ncpus = 2, ndigits = 3, updateby = 1000)

Arguments

fullpost1	A 2-component list object resulting from FitAllShrink
fullpost2	A 2-component list object resulting from FitAllShrink
shrinksimul	A list object resulting from ShrinkSeq or ShrinkGauss
para	Character string containing the name of the parameter for which combined posteriors are desired.
includelincomb	Boolean indicating whether combined posteriors for linear combinations are desired.
margcomb	Character string. Either "marginals.fixed", "marginals.random", "marginals.hyper" or "marginals.internal.hyper".
ncpus	Integer. The number of cpus to use for parallel computations.
ndigits	Integer. Numerical precision in digits for the output.
updateby	Integer. Perform the computation in blocks of size updateby. In particular useful when fullpost1 and fullpost2 are large objects.

Details

The parameter names of hyper-parameters like zero-inflation, random effects precision and size of the negative binomial are automatically generated by INLA. The names may be somewhat incovenient. Please check these, e.g. by using names(fullpost1[[1]][[1]]$marginals.hyper).

Value

Two-component list object.

res	A list that contains the combined posteriors in numerical format
priors	A list passing through all information on priors present in fullpost1

Author(s)

Mark A. van de Wiel

References

Van de Wiel MA, Leday GGR, Pardo L, Rue H, Van der Vaart AW, Van Wieringen WN (2012). Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Biostatistics.

See Also

FitAllShrink, ShrinkSeq, ShrinkGauss,

Examples

## Not run: 
#See Van de Wiel et al. (2012) for more details on the data and the analysis
#To empty current R memory and enlarge memory.limit (Windows) consider, before applying 'ShrinkBayes', using 
#rm(list=ls());gc();
#memory.limit(size = 4000)

#loads brain sequencing data (CAGE); 10,000 rows (tag clusters), 25 samples
data(CAGEdata10000)
CAGEdata <- CAGEdata10000

#FOR FIRST TRY: USE 1000 DATA ROWS ONLY
CAGEdata <- CAGEdata[1:1000,]

#loads design of the brain study; includes covariates
data(design_brain)

#retrieves covariates from the design matrix
pers <- design_brain$pers  #persons
batch<-design_brain$batch   #batch
groupfac <- design_brain$groupfac #group (= brain region)

#Number of cpus to use in (parallel) computations
ncpus2use <- 10

#redefines baseline level as '0' such that it is in further analysis always used as baseline. 
groupfac <- BaselineDef("groupfac",baselinegroup="1")

#By default only comparisons with the baseline are included. Use the following convenience function to create the other 
#pair-wise comparisons for a given factor when this contains 3 or more levels (groups)
lincombvec <- AllComp("groupfac")

#Formula as used by INLA
form = y ~ 1 + groupfac + batch + f(pers,model="iid") #'batch' and 'group' as fixed effect, 'pers' as random effect

#Simultaneous hrinkage for 'groupfac' and 'pers'. In addition, the negative binomial overdispersion is shrunken by default (see shrinkdisp argument). 
#In the example below we allow a mixture prior for overdispersion (mixtdisp=TRUE). This increases computing time by a factor 2.
#INCREASE maxiter FOR OBTAINING FINAL RESULTS (DEFAULT = 15)
shrinksimul <- ShrinkSeq(form=form, dat=CAGEdata,shrinkfixed="groupfac",shrinkrandom="pers",mixtdisp=TRUE,ncpus=ncpus2use, maxiter=3)

#The mass on the point mass of the mixture prior for overdispersion; 
#if close to zero, then only fits on (zi-)-negative binomial are needed; otherwise (zi-)Poisson is also needed
shrinksimul$pmlist$mixp

#Fit all using the priors resulting from ShrinkSeq and zi-poisson likelihood; also find posteriors for the linear combinations defined above
fitzip <- FitAllShrink(form,dat=CAGEdata,fams="zip",shrinksimul,ncpus=ncpus2use,lincomb=lincombvec)

#Fit all using the priors resulting from ShrinkSeq and zi-nb likelihood; also find posteriors for the linear combinations defined above
fitzinb <- FitAllShrink(form,dat=CAGEdata,fams="zinb",shrinksimul,ncpus=ncpus2use,lincomb=lincombvec)

#Combine posteriors from fitzip and fitzinb; linear combinations are included by default
cp <- CombinePosteriors(fitzip,fitzinb,shrinksimul,para="groupfac",ncpus=ncpus2use) 

#Find nonparametric prior for all group-wise differences. These include the linear combinations. 
#Please check lcfac argument (defaults to 2): should be sum of squares of weights in the lincombvec (= 2 for pairwise contrasts)
#INCREASE maxiter FOR OBTAINING FINAL RESULTS (DEFAULT = 15)
npprior <- NonParaUpdatePrior(fitall=cp,modus="fixed", shrinkpara="groupfac", shrinklc=TRUE,ncpus=ncpus2use, maxiter = 3)

#Update posteriors using the nonparametric prior
nppostshr <- NonParaUpdatePosterior(cp,npprior,ncpus=ncpus2use)

#Compute local fdrs ( = posterior null (tail-)probabilities under shrinkage prior)
#Here P(theta <= 0.1) is computed, assuming {theta<=0.1} is the null-domain.
lfdr <- SummaryWrap(nppostshr, thr = 0.1)

#Compute Bayesian FDRs; all BFDRs for each comparison [matrix format]
BFDRs <- BFDR(lfdr)

#Compute Bayesian FDRs; BFDR for multiple comparisons
BFDRmult <- BFDR(lfdr,multcomp=TRUE)

## End(Not run)

markvdwiel/ShrinkBayes documentation built on March 27, 2022, 7:47 p.m.