DIME.plot.fit {DIME}

Plot Best Model Goodness of Fit

Description

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.

Usage

DIME.plot.fit(data, obj, ...)

Arguments

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 DIME function.

...

additional graphical arguments to be passed to methods (see par).

Details

The components representing differential data are denoted by asterisk (*) symbol on the plot legend.

Author(s)

Cenny Taslim [e-mail], with contributions from Abbas Khalili [e-mail], Dustin Potter [e-mail], and Shili Lin [e-mail]

See Also

DIME, gng.plot.fit,inudge.plot.fit

Examples

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);