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\name{PolyFitPlot}
\alias{PolyFitPlot}
\title{
Fit the mean-var relationship using polynomial regression
}
\description{
'PolyFitPlot' fits the mean-var relationship using polynomial regression.
}
\usage{
PolyFitPlot(X, Y, nterms, xname = "Estimated Mean",
yname = "Estimated Var", pdfname = "",
xlim = c(-1,5), ylim = c(-1,7), ChangeXY = F,
col = "red")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{X}{
The first group of values want to be fitted by the polynomial regression (e.g Mean of the data).
}
\item{Y}{
The second group of values want to be fitted by the polynomial regression (e.g. variance of the data). The length of Y should be the same as the length of X.
}
\item{nterms}{
How many polynomial terms want to be used.
}
\item{xname}{
Name of the x axis.
}
\item{yname}{
Name of the y axis.
}
\item{pdfname}{
Name of the plot.
}
\item{xlim}{
The x limits of the plot.
}
\item{ylim}{
The y limits of the plot.
}
\item{ChangeXY}{
If ChangeXY is setted to be TRUE, X will be treated as the dependent variable and Y will be treated as the independent one. Default is FALSE.
}
\item{col}{
Color of the fitted line.
}
}
\value{The PolyFitPlot function provides a smooth scatter plot of two variables and their best fitting line of polynomial regression.
}
\references{
Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013)
}
\author{
Ning Leng
}
\examples{
data(IsoList)
str(IsoList)
IsoMat = IsoList$IsoMat
IsoNames = IsoList$IsoNames
IsosGeneNames = IsoList$IsosGeneNames
IsoSizes = MedianNorm(IsoMat)
NgList = GetNg(IsoNames, IsosGeneNames)
IsoNgTrun = NgList$IsoformNgTrun
#IsoEBOut = EBTest(Data = IsoMat.small,
# NgVector = IsoNgTrun,
# Conditions = as.factor(rep(c("C1","C2"), each=5)),
# sizeFactors = IsoSizes, maxround = 5)
#par(mfrow=c(2,2))
#PolyFitValue = vector("list",3)
#for(i in 1:3)
# PolyFitValue[[i]] = PolyFitPlot(IsoEBOut$C1Mean[[i]],
# IsoEBOut$C1EstVar[[i]], 5)
#PolyAll = PolyFitPlot(unlist(IsoEBOut$C1Mean),
# unlist(IsoEBOut$C1EstVar), 5)
#lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]),
# PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],
# col="yellow", lwd=2)
#lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]),
# PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],
# col="pink", lwd=2)
#lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]),
# PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],
# col="green", lwd=2)
#legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"),
# col = c("red","yellow","pink","green"),
# lty=1, lwd=3, box.lwd=2)
}
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