DIME (version 1.3.0)

gng.plot.fit: Plot GNG Goodness of Fit

Description

Plot the estimated GNG mixture model fitted using gng.fit along with it's 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

gng.plot.fit(data, obj, resolution = 100, breaks = 100, legpos = NULL,
  xlim = NULL, main=NULL, ...)

Arguments

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 gng.fit function.

resolution

optional bandwidth used to estimate the density function. Higher number smoother curve.

breaks

optional see hist, breaks parameters for histogram plot.

legpos

optional vector of (x,y) location for the legend position

xlim

optional x-axis limit (see par).

main

optional plot title (see par).

…

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.

See Also

gng.plot.comp, gng.plot.mix, hist

Examples

# NOT RUN {
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 with 2-normal components
bestGng <- gng.fit(data,K=2);

# Goodness of fit plot
gng.plot.fit(data,bestGng);
# }