R: Plots three diagnostic plots to check the validity of the...
diagnostic.mcmc
R Documentation
Plots three diagnostic plots to check the validity of the assumptions of linear model analysis.
Description
Predicted vs observed plot tests for linearity, Scale-location plot tests for homoscedasticity, and Normal QQ plot tests for normality of the residuals.
Usage
diagnostic.mcmc(model, ...)
Arguments
model
MCMCglmm object (a model fitted by mcmc.qpcr or mcmc.qpcr.gauss), obtained with additional options, 'pl=T, pr=T'
...
Various plot() options to modify color, shape and size of the plotteed points.
Value
A plot with three panels.
Author(s)
Mikhail V. Matz, UT Austin
<matz@utexas.edu>
References
Matz MV, Wright RM, Scott JG (2013) No Control Genes Required: Bayesian Analysis of qRT-PCR Data. PLoS ONE 8(8): e71448. doi:10.1371/journal.pone.0071448
Examples
# loading Cq data and amplification efficiencies
data(coral.stress)
data(amp.eff)
# extracting a subset of data
cs.short=subset(coral.stress, timepoint=="one")
genecolumns=c(5,6,16,17) # specifying columns corresponding to genes of interest
conditions=c(1:4) # specifying columns containing factors
# calculating molecule counts and reformatting:
dd=cq2counts(data=cs.short,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37)
# fitting the model
mm=mcmc.qpcr(
fixed="condition",
data=dd,
controls=c("nd5","rpl11"),
pr=TRUE,pl=TRUE, # these flags are necessary for diagnostics
nitt=4000 # remove this line when analyzing real data!
)
diagnostic.mcmc(mm)
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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MCMC.qpcr)
Loading required package: MCMCglmm
Loading required package: Matrix
Loading required package: coda
Loading required package: ape
Loading required package: ggplot2
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MCMC.qpcr/diagnostic.mcmc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: diagnostic.mcmc
> ### Title: Plots three diagnostic plots to check the validity of the
> ### assumptions of linear model analysis.
> ### Aliases: diagnostic.mcmc
>
> ### ** Examples
>
>
> # loading Cq data and amplification efficiencies
> data(coral.stress)
> data(amp.eff)
> # extracting a subset of data
> cs.short=subset(coral.stress, timepoint=="one")
>
> genecolumns=c(5,6,16,17) # specifying columns corresponding to genes of interest
> conditions=c(1:4) # specifying columns containing factors
>
> # calculating molecule counts and reformatting:
> dd=cq2counts(data=cs.short,genecols=genecolumns,
+ condcols=conditions,effic=amp.eff,Cq1=37)
>
> # fitting the model
> mm=mcmc.qpcr(
+ fixed="condition",
+ data=dd,
+ controls=c("nd5","rpl11"),
+ pr=TRUE,pl=TRUE, # these flags are necessary for diagnostics
+ nitt=4000 # remove this line when analyzing real data!
+ )
$PRIOR
$PRIOR$B
$PRIOR$B$mu
[1] 0 0 0 0 0 0 0 0
$PRIOR$B$V
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0.0000000 0.0000000
[2,] 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0.0000000 0.0000000
[3,] 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0.0000000 0.0000000
[4,] 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0.0000000 0.0000000
[5,] 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0.0000000 0.0000000
[6,] 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0.0000000 0.0000000
[7,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0.3663091 0.0000000
[8,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0.0000000 0.3663091
$PRIOR$R
$PRIOR$R$V
[,1] [,2] [,3] [,4]
[1,] 1 0 0 0
[2,] 0 1 0 0
[3,] 0 0 1 0
[4,] 0 0 0 1
$PRIOR$R$nu
[1] 3.002
$PRIOR$G
$PRIOR$G$G1
$PRIOR$G$G1$V
[1] 1
$PRIOR$G$G1$nu
[1] 0
$FIXED
[1] "count~0+gene++gene:condition"
$RANDOM
[1] "~sample"
MCMC iteration = 0
Acceptance ratio for liability set 1 = 0.000500
Acceptance ratio for liability set 2 = 0.000516
Acceptance ratio for liability set 3 = 0.000194
Acceptance ratio for liability set 4 = 0.000313
MCMC iteration = 1000
Acceptance ratio for liability set 1 = 0.100469
Acceptance ratio for liability set 2 = 0.379032
Acceptance ratio for liability set 3 = 0.308935
Acceptance ratio for liability set 4 = 0.115406
MCMC iteration = 2000
Acceptance ratio for liability set 1 = 0.158500
Acceptance ratio for liability set 2 = 0.422839
Acceptance ratio for liability set 3 = 0.358935
Acceptance ratio for liability set 4 = 0.173063
MCMC iteration = 3000
Acceptance ratio for liability set 1 = 0.185656
Acceptance ratio for liability set 2 = 0.432645
Acceptance ratio for liability set 3 = 0.366387
Acceptance ratio for liability set 4 = 0.204250
MCMC iteration = 4000
Acceptance ratio for liability set 1 = 0.200375
Acceptance ratio for liability set 2 = 0.439484
Acceptance ratio for liability set 3 = 0.338484
Acceptance ratio for liability set 4 = 0.231406
> diagnostic.mcmc(mm)
>
>
>
>
>
>
> dev.off()
null device
1
>