Plotting the likelihood along MCMC sampling.

Description

Plots the log likelihood along MCMC sampling.

Usage

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

Arguments

Author(s)

Benedikt Zacher zacher@lmb.uni-muenchen.de

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")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(birta)
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, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, 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, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

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

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/birta/plotConvergence.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotConvergence
> ### Title: Plotting the likelihood along MCMC sampling.
> ### Aliases: plotConvergence
> ### Keywords: hplot
> 
> ### ** 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)
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 =  1.103331  beta =  0.9837442  n0 =  1 
MCMC parameters: burnin =  10000 niter =  50000 thin =  50 condition specific inference =  TRUE 

sampling ...
No edge weight adjustment!
finished.
> plotConvergence(sim_result, nburnin=10000, title="simulation")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>