Plots three diagnostic plots to check the validity of the assumptions of linear model analysis.
Predicted vs observed plot tests for linearity, Scale-location plot tests for homoscedasticity, and Normal QQ plot tests for normality of the residuals.
diagnostic.mcmc(model, ...)
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. |
A plot with three panels.
Mikhail V. Matz, UT Austin <matz@utexas.edu>
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
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.