gng.plot.fit {DIME} | R Documentation |
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 |
resolution |
optional bandwidth used to estimate the density function. Higher number smoother curve. |
breaks |
optional see |
legpos |
optional vector of (x,y) location for the legend position |
xlim |
optional x-axis limit (see |
main |
optional plot title (see |
... |
additional graphical arguments to be passed to methods (see |
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
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);