nudge.plot.fit | R Documentation |
Plot the estimated NUDGE mixture model fitted using nudge.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.
nudge.plot.fit(data, obj, resolution = 100, breaks = 100, xlim = NULL, legpos = NULL, ...)
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 |
xlim |
optional limit for the x-axis. |
legpos |
optional vector of (x,y) location for the legend position |
... |
additional graphical arguments to be passed to methods (see |
The components representing differential data are denoted by asterisk (*) symbol on the plot legend.
nudge.plot.comp
, nudge.plot.mix
, hist
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); # goodness of fit plot nudge.plot.fit(data,bestNudge,breaks=40);
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