plotConvergence: Plotting the likelihood along MCMC sampling.

In birta: Bayesian Inference of Regulation of Transcriptional Activity

Description

Plots the log likelihood along MCMC sampling.

Usage

plotConvergence(res, nburnin=NULL, title="")

Arguments

res	The result from birta.run (a list).
nburnin	Number of iterations used for the burn in.
title	Optional title of the plot.

Author(s)

Benedikt Zacher [e-mail]

See Also

birta

Examples

data(humanSim)
data(humanSim)
design = model.matrix(~0+factor(c(rep("control", 5), rep("treated", 5))))
colnames(design) = c("control", "treated")
contrasts = "treated - control"
limmamRNA = limmaAnalysis(sim$dat.mRNA, design, contrasts)
limmamiRNA = limmaAnalysis(sim$dat.miRNA, design, contrasts)
sim_result = birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA=limmamRNA, 
 limmamiRNA=limmamiRNA, nrep=c(5,5,5,5), genesets=genesets, 
 model="all-plug-in", niter=50000, nburnin=10000, 
 sample.weights=FALSE, potential_swaps=potential_swaps)
plotConvergence(sim_result, nburnin=10000, title="simulation")

Example output

Loading required package: limma
Loading required package: MASS
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Formatting regulator-target network -> checking overlap between network and measurements.
30  DE gene(s) have  69 regulating TFs and  328 regulating miRNAs

BIRTA
Data and network: #mRNAs =  1000 #miRNAs =  553 #TFs =  156 only one weight per regulator =  TRUE 
Prior parameters: theta_TF =  0.2211538 theta_miRNA =  0.2965642 lambda =  0 
Hyperparameters: alpha =  1.101457  beta =  0.9927876  n0 =  1 
MCMC parameters: burnin =  10000 niter =  50000 thin =  50 condition specific inference =  TRUE 

sampling ...
No edge weight adjustment!
finished.

birta documentation built on April 28, 2020, 7:27 p.m.