View source: R/DIME.plot.fit.R
DIME.plot.fit | R Documentation |
Plot the best mixture model fitted using DIME
along with their
estimated individual components, superimposed on the histogram of the
observation data. This plot shows how good the fit of the estimated model to the
data.
DIME.plot.fit(data, obj, ...)
data |
an R list of vector of normalized intensities (counts). Each element can correspond to particular chromosome. User can construct their own list containing only the chromosome(s) they want to analyze. |
obj |
a list object returned by |
... |
additional graphical arguments to be passed to methods (see |
The components representing differential data are denoted by asterisk (*) symbol on the plot legend.
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
DIME
, gng.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])); 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])); data <- list(chr1,chr3); # run DIME with small maximum iterations and repetitions set.seed(1234); test <- DIME(data,gng.max.iter=10,gng.rep=1,inudge.max.iter=10,inudge.rep=1, nudge.max.iter=10,nudge.rep=1); # plot best model DIME.plot.fit(data,test);
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