inudge.plot.mix | R Documentation |
Plot each estimated individual components of iNUDGE mixture model fitted using
inudge.fit
.
inudge.plot.mix(obj, amplify = 1, resolution = 100, new.plot = TRUE, ...)
obj |
a list object returned by |
amplify |
optional scaling factor for visualization purposes. |
resolution |
optional bandwidth used to estimate the density function. Higher number makes a smoother curve. |
new.plot |
optional logical variable on whether to create new plot. |
... |
additional graphical arguments to be passed to methods (see |
inudge.plot.comp
, inudge.plot.fit
,
inudge.plot.qq
, DIME.plot.fit
, gng.plot.fit
.
library(DIME) # generate simulated datasets with underlying uniform and 2-normal distributions set.seed(1234); N1 <- 1500; N2 <- 500; rmu <- c(-2.25,1.5); rsigma <- c(1,1); rpi <- c(.10,.45,.45); a <- (-6); b <- 6; chr4 <- list(c(-runif(ceiling(rpi[1]*N1),min = a,max =b), rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2]))); chr9 <- list(c(-runif(ceiling(rpi[1]*N2),min = a,max =b), rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]))); # analyzing chromosome 4 and 9 data <- list(chr4,chr9); # fit iNUDGE model set.seed(1234); bestInudge <- inudge.fit(data, K = 2, max.iter=20); # plot estimated iNUDGE model imposed on the histogram of observed data hist(unlist(data),freq=FALSE,breaks=40); inudge.plot.mix(bestInudge,resolution=1000,new.plot=FALSE,col="red");
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