inudge.plot.fit | R Documentation |
Plot the estimated iNUDGE mixture model fitted using inudge.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.
inudge.plot.fit(data, obj, resolution = 100, breaks = 100, legpos = NULL, xlim = NULL, main = NULL,...)
data |
an R list of vector of normalized intensities (counts). Each element can correspond to 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 |
The components representing differential data are denoted by asterisk (*) symbol on the plot legend.
gng.plot.comp
, gng.plot.mix
,
hist
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 with 2-normal components and maximum iterations = 20 set.seed(1234); bestInudge <- inudge.fit(data, K = 2, max.iter=20); # Goodness of fit plot inudge.plot.fit(data,bestInudge,legpos=c(-6,0.3),ylim=c(0,0.3),breaks=40);
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