gng.plot.mix | R Documentation |
Plot each estimated individual components of GNG mixture model fitted using
gng.fit
.
gng.plot.mix(obj, amplify = 1, resolution = 100, new.plot = TRUE, ...)
obj |
a list object returned by |
amplify |
optional scaling factor for visualization purposes. |
resolution |
optional bandwidth used to estimate the density function. Higher number makes a smoother curve. |
new.plot |
optional logical variable on whether to create new plot. |
... |
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.plot.mix
, gng.plot.comp
, gng.plot.fit
,
gng.plot.qq
, DIME.plot.fit
, inudge.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); # Plot the estimated GNG model imposed on the histogram of the observed data hist(unlist(data),freq=FALSE,breaks=100,xlim=c(-20,20)) gng.plot.mix(bestGng,resolution=1000,new.plot=FALSE,col="red");
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