plot.mixEM {mixtools} | R Documentation |
Various Plots Pertaining to Mixture Models
Description
Takes an object of class mixEM
and returns various graphical output for select mixture models.
Usage
## S3 method for class 'mixEM':
plot(x, whichplots = 1,
loglik = 1 %in% whichplots,
density = 2 %in% whichplots,
xlab1="Iteration", ylab1="Log-Likelihood",
main1="Observed Data Log-Likelihood", col1=1, lwd1=2,
xlab2=NULL, ylab2=NULL, main2=NULL, col2=NULL,
lwd2=2,
alpha = 0.05, marginal = FALSE, ...)
Arguments
x |
An object of class mixEM . |
whichplots |
vector telling which plots to produce: 1 = loglikelihood
plot, 2 = density plot. Irrelevant if loglik and density
are specified. |
loglik |
If TRUE, a plot of the log-likelihood versus the EM iterations is given. |
density |
Graphics pertaining to certain mixture models. The details are given below. |
xlab1, ylab1, main1, col1, lwd1 |
Graphical parameters xlab , ..., lwd
to be passed to the loglikelihood plot. Trying to change these parameters using
xlab , ..., lwd will result in an error, but all other graphical parameters
are passed directly to the plotting functions via ... |
xlab2, ylab2, main2, col2, lwd2 |
Same as xlab1 etc. but for the
density plot |
alpha |
A vector of significance levels when constructing confidence ellipses and confidence bands for the mixture
of multivariate normals and mixture of regressions cases, respectively. The default is 0.05. |
marginal |
For the mixture of bivariate normals, should optional marginal histograms be included? |
... |
Graphical parameters passed to plot command. |
Value
plot.mixEM
returns a plot of the log-likelihood versus the EM iterations by default for all objects of class
mixEM
. In addition, other plots may be produced for the following k-component mixture model functions:
normalmixEM |
A histogram of the raw data is produced along with k density curves determined by normalmixEM . |
repnormmixEM |
A histogram of the raw data produced in a similar manner as for normalmixEM . |
mvnormalmixEM |
A 2-dimensional plot with each point color-coded to denote its most probable component membership. In
addition, the estimated component means are plotted along with (1 - alpha )% bivariate normal density contours. These ellipses are
constructed by assigning each value to their component of most probable membership and then using normal theory. Optional marginal histograms
may also be produced. |
regmixEM |
A plot of the response versus the predictor with each point color-coded to denote its most probable component
membership. In addition, the estimated component regression lines are plotted along with (1 - alpha )% Working-Hotelling
confidence bands. These bands are constructed by assigning each value to their component of most probable membership and then
performing least squares estimation. |
logisregmixEM |
A plot of the binary response versus the predictor with each point color-coded to denote its most probable
compopnent membership. In addition, the estimate component logistic regression lines are plotted. |
regmixEM.mixed |
Provides a 2x2 matrix of plots summarizing the posterior slope and posterior intercept terms from a
mixture of random effects regression. See post.beta for a more detailed description. |
See Also
post.beta
Examples
##Analyzing the Old Faithful geyser data with a 2-component mixture of normals.
data(faithful)
attach(faithful)
out<-normalmixEM(waiting, arbvar = FALSE, verb = TRUE,
epsilon = 1e-04)
plot(out, density = TRUE, w = 1.1)
##Fitting randomly generated data with a 2-component location mixture of bivariate normals.
x.1<-rmvnorm(40, c(0, 0))
x.2<-rmvnorm(60, c(3, 4))
X.1<-rbind(x.1, x.2)
out.1<-mvnormalmixEM(X.1, arbvar = FALSE, verb = TRUE,
epsilon = 1e-03)
plot(out.1, density = TRUE, alpha = c(0.01, 0.05, 0.10),
marginal = TRUE)
[Package
mixtools version 0.3.3
Index]