plot.mixEM: Various Plots Pertaining to Mixture Models

In mixtools: Tools for Analyzing Finite Mixture Models

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)
set.seed(100)
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)

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.