# nudge.plot.mix: Plot NUDGE Mixture Component Function In DIME: DIME (Differential Identification using Mixture Ensemble)

## Description

Plot each estimated individual components of NUDGE mixture model fitted using `nudge.fit`.

## Usage

 `1` ```nudge.plot.mix(obj, amplify = 1, resolution = 100, new.plot = TRUE, ...) ```

## Arguments

 `obj` a list object returned by `nudge.fit` function. `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 `par`).

`nudge.plot.comp`, `nudge.plot.fit`, `nudge.plot.qq`, `DIME.plot.fit`, `gng.plot.fit`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```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 only bestNudge <- nudge.fit(data, max.iter=20); # plot estimated iNUDGE model imposed on the histogram of observed data hist(unlist(data),freq=FALSE,breaks=40); nudge.plot.mix(bestNudge,resolution=1000,new.plot=FALSE,col="red"); ```