nudge.plot.qq: QQ-plot of GNG model vs. observed data

In DIME: Differential Identification using Mixture Ensemble

QQ-plot of GNG model vs. observed data

Description

Produces a QQ-plot for visual inspection of quality of fit with regards to the uniform Gaussian (NUDGE) mixture model estimated using the function nudge.fit

Usage

nudge.plot.qq(data, obj, resolution = 10, xlab = NULL, ylab = NULL, 
  main = NULL, pch = 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 number of points used to sample the estimated density function.
xlab	optional x-axis label (see par).
ylab	optional y-axis label (see par).
main	optional plot title (see par).
pch	optional plotting character, i.e., symbol to use (see par).
...	additional graphical arguments to be passed to methods (see par).

See Also

nudge.fit, qqplot

Examples

library(DIME)

# generate simulated datasets with underlying uniform and 1-normal components
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(1.5); rsigma <- c(1); 
rpi <- c(.10,.90); a <- (-6); b <- 6; 
chr1 <- c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
  rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]));
chr4 <- c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
  rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]));  
# analyzing chromosome 1 and 4
data <- list(chr1,chr4);

# fit NUDGE model with maximum iterations = 20
set.seed(1234);
bestNudge <- nudge.fit(data, max.iter=20);

# QQ-plot
nudge.plot.qq(data,bestNudge);

DIME documentation built on May 9, 2022, 5:05 p.m.