HPDpoints {MCMC.qpcr}

HPDplot, HPDpoints

Description

Calculates and plots posterior means with 95% credible intervals for specified fixed effects (or their combination) for all genes. HPDpoints only adds graphs to an existing plot.

Usage

HPDpoints(model, factors, factors2 = NULL, ylimits = NULL, 
hpdtype = "w", inverse = F, jitter = 0, ...)

Arguments

model

The output of mcmc.qpcr function.

factors

A vector of names of fixed effects of interest; see details.

factors2

A second vector of fixed effect names to be subtracted from the first; see details.

ylimits

Y-limits for the plot such as c(-3,6); autoscale by default.

hpdtype

Specify hpdtype="l" to plot the upper and lower 95% credible limits as a continuous dashed line across all genes. By default (hpdtype="w") the limits are plotted as whiskers around each point.

inverse

Plot the inverse of the result.

jitter

For hpdtype="w", shifts the plotted values and whiskers by the specified distance along the x axis (reasonable jitter values are 0.15 or -0.15, for example).

...

Various plot() options; such as col (color of lines and symbols), pch (type of symbol), main (plot title) etc.

Details

See details in HPDplot()

Value

A graph added to a plot.

Author(s)

Mikhail V. Matz, UT Austin <[e-mail]>

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"),
	nitt=4000 # remove this line when analyzing real data!
)

# plotting log2(fold change) in response to heat stress for all genes
HPDplot(model=mm,factors="conditionheat",main="response to heat stress")