plmDE (version 1.0)

plot.DEresults: Plot Fit of DEresults Model

Description

Given a model contained in a DEresults object, plot.DEresults plots the fit of the model on the expression data for a specified gene/probe.

Usage

"plot"(x, covariate, geneNumber = 1, plmDEobject, 
loess = TRUE, legend = TRUE, legend.coor = "topright", ...)

Arguments

x

An object of type DEresults containing the model whose fitted values we wish to plot.

covariate

The covariate we wish to plot against the expression level data.

geneNumber

The index of the gene whose expression data should be plotted on the y-axis.

plmDEobject

An object of type plmDE containing all the data on expression and measurements of the covariates.

loess

Should a loess fit on the covariate and actual expression level data be plotted?

legend

Should a legend be plotted?

legend.coor

the coordinates of the legend. See legend for details.

...

parameters to be passed to plot

See Also

fitGAPLM, plmDE

Examples

## create an object of type \code{plmDE} containing disease with "control"
## and "disease" groups with measures of weight and severity. Then fit model:
ExpressionData = as.data.frame(matrix(abs(rnorm(10000, 1, 1.5)), ncol = 100))
names(ExpressionData) = sapply(1:100, function(x) paste("Sample", x))
Genes = sapply(1:100, function(x) paste("Gene", x))
DataInfo = data.frame(sample = names(ExpressionData), group = c(rep("Control", 50), 
rep("Diseased", 50)), weight = abs(rnorm(100, 50, 20)), severity = c(rep(0, 50), 
abs(rnorm(50, 100, 20))))
plmDEobject = plmDEmodel(Genes, ExpressionData, DataInfo)
model = fitGAPLM(plmDEobject, continuousCovariates.fullModel = c("weight", "severity"),
 compareToReducedModel = TRUE, indicators.reducedModel = NULL, 
continuousCovariates.reducedModel = "weight")
plot(model, "weight", 6, plmDEobject)