plot.DEresults: Plot Fit of 'DEresults' Model

In plmDE: Additive partially linear models for differential gene expression analysis

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

## S3 method for class 'DEresults'
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

Author(s)

Jonas Mueller

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)

plmDE documentation built on May 29, 2017, 6:37 p.m.