PolyFitPlot: Fit the mean-var relationship using polynomial regression

Description Usage Arguments Value Author(s) References Examples

Description

'PolyFitPlot' fits the mean-var relationship using polynomial regression.

Usage

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PolyFitPlot(X, Y, nterms, xname = "Estimated Mean", 
	yname = "Estimated Var", pdfname = "", 
	xlim =  c(-1,5), ylim = c(-1,7), ChangeXY = F, 
	col = "red")

Arguments

X

The first group of values want to be fitted by the polynomial regression (e.g Mean of the data).

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.

nterms

How many polynomial terms want to be used.

xname

Name of the x axis.

yname

Name of the y axis.

pdfname

Name of the plot.

xlim

The x limits of the plot.

ylim

The y limits of the plot.

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.

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.

Author(s)

Ning Leng

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)

Examples

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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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