gng.plot.qq | R Documentation |
Produces a QQ-plot for visual inspection of quality of fit with regards to
the exponential Gaussian (GNG) mixture model estimated using the function
gng.fit
gng.plot.qq(data, obj, resolution=10, xlab=NULL, ylab=NULL, main=NULL, pch=NULL,...)
data |
an R list of vector of normalized intensities (counts). Each element can correspond to a particular chromosome. User can construct their own list containing only the chromosome(s) they want to analyze. |
obj |
a list object returned by |
resolution |
optional number of points used to sample the estimated density function. |
xlab |
optional x-axis label (see |
ylab |
optional y-axis label (see |
main |
optional plot title (see |
pch |
optional plotting symbol to use (see |
... |
additional graphical arguments to be passed to methods (see |
Cenny Taslim taslim.2@osu.edu, with contributions from Abbas Khalili khalili@stat.ubc.ca, Dustin Potter potterdp@gmail.com, and Shili Lin shili@stat.osu.edu
gng.fit
, gng.plot.fit
library(DIME) # generate simulated datasets with underlying exponential-normal components N1 <- 1500; N2 <- 500; K <- 4; rmu <- c(-2.25,1.50); rsigma <- c(1,1); rpi <- c(.05,.45,.45,.05); rbeta <- c(12,10); set.seed(1234); chr1 <- c(-rgamma(ceiling(rpi[1]*N1),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N1),shape = 1,scale = rbeta[2])); chr2 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2])); chr3 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2])); # analyzing only chromosome 1 and chromosome 3 data <- list(chr1,chr3); # Fitting a GNG model only bestGng <- gng.fit(data,K=2); # QQ-plot gng.plot.qq(data,bestGng)
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