plotFitted {polySegratioMM}R Documentation

Plot observed segregation ratios and fitted and theoretical models

Description

Plots histogram of observed segregation ratios on logit scale along with scaled density of fitted components corresponding to dosage classes. Plots of expected theoretical distributions can be plotted with or without segregation ratio data.

Usage


## S3 method for class 'runJagsWrapper':
plot(x, theoretical=FALSE, ...)

plotFitted(seg.ratios, summary.mixture, add.random.effect=TRUE,
  theoretical=FALSE, model=NULL, theory.col="red",
  xaxis=c("logit","raw"), ylim=NULL, NCLASS=NULL, n.seq=100,
  xlab="logit(Segregation Ratio)", ylab="Density", density.plot=FALSE,
  fitted.lwd=2, fitted.col="blue", bar.col="lightgreen", cex=1,
  warnings = FALSE, main=NULL, ...)

plotTheoretical(ploidy.level=8, seg.ratios=NULL, n.components=NULL,
  expected.segratio=NULL, proportions=c(0.65,0.2,0.1,0.03,0.01,0.01, 0, 0),
  n.individuals=200, xaxis=c("raw","logit"),
  type.parents=c("heterogeneous","homozygous"), xlim=c(0,1),
  NCLASS=NULL, xlab="Segregation Ratio", ylab="Density",
  density.plot=FALSE, fitted.lwd=2, fitted.col="blue", cex=1,
  warnings = TRUE, main=NULL, ...)

Arguments

x object of class runJagsWrapper produced by using runSegratioMM to set up and fit mixture model
seg.ratios segregation ratios as class segRatio
summary.mixture mcmc summary data produce by summary.segratioMCMC
add.random.effect add random variance component to fitted distribution plot if model includes a random effect (default: TRUE)
theoretical whether to plot the expected theoretical distribution under the fitted model (default: FALSE)
model object of class modelSegratioMM specifying model if plotting expected theoretical distribution
theory.col colour for expected theoretical distribution (default: "red")
ploidy.level the number of homologous chromosomes
n.components number of components for mixture model
expected.segratio may be specified or automatically calculated from ploidy level etc
xaxis whether to plot on "logit" or "raw" scale. Defaults to "logit" if plotting segregation ratios or "raw" for theoretical distributions
proportions for no. of markers in each component of theoretical distribution plot
n.individuals for theoretical distribution plot - taken from segregation ratios if supplied
type.parents "heterogeneous" if parental markers are 0,1 or "homogeneous" if parental markers are both 1
ylim c(lower,upper) yaxis limits for histogram of segregation ratios
xlim c(lower,upper) xaxis limits for segregation ratios
NCLASS number of classes for histogram (Default: 100)
n.seq number of points to use for plotting fitted mixture
xlab x-axis label
ylab y-axis label
density.plot whether to plot a smoothed density as well as segregation data and fitted and/or theoretical distributions (default: FALSE)
main title for plot
fitted.lwd width for fitted line
fitted.col colour for fitted line
bar.col colour for histogram
cex character expansion for text (see par)
warnings print warnings like number of components etc (Default: FALSE)
... extra options for plot

Details

plotFitted plot histogram of observed segregation ratios on logit scale along with scaled density of fitted components corresponding to dosage classes using trellis

plotTheoretical plot expected distribution of autopolyploid dominant markers on probability (0,1) scale. Segregation ratios may also be plotted

plot.runJagsWrapper plots the fitted values of object of class runJagsWrapper which has been produced by using runSegratioMM to set up and fit mixture model

Note that since trellis graphics are employed, plots may need to be printed in order to see them

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

summary.mcmc mcmc segratioMCMC readJags diagnosticsJagsMix runSegratioMM

Examples

## simulate small autooctaploid data set
plotTheoretical(8, proportion=c(0.7,0.2,0.1),n.individuals=50)
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## fit simple model in one hit and summarise
## Not run: 
x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)

## plot fitted model using 'plotFitted'
plotFitted(sr, x.run$summary)
a.plot <- plotFitted(sr, x.run$summary, density.plot=TRUE)
print(a.plot)
## or the easier way
plot(x.run, theoretical=TRUE)
## End(Not run)

[Package polySegratioMM version 0.5-2 Index]