mixtools (version 2.0.0)

plotly_mixEM: Visualization of output of mixEM function using plotly

Description

This is an updated version of plot.mixEM. For more technical details, please refer to plot.mixEM.

Usage

plotly_mixEM(x, 
             loglik = TRUE,
             density = FALSE,
             xlab1="Iteration", xlab1.size=15 , xtick1.size=15,
             ylab1="Log-Likelihood", ylab1.size=15 , ytick1.size=15,
             title1="Observed Data Log-Likelihood", title1.size=15,
             title1.x = 0.5,title1.y=0.95,
             col1="#1f77b4", lwd1=3, cex1=6,
             xlab2=NULL, xlab2.size=15 , xtick2.size=15,
             ylab2=NULL, ylab2.size=15 , ytick2.size=15,
             title2=NULL, title2.size=15,
             title2.x = 0.5,title2.y=0.95, col.hist = "#1f77b4",
             col2=NULL, lwd2=3, cex2=6,
             alpha = 0.05, marginal = FALSE)

Value

A plot of the output of mixEM function is presented depends on output type.

Arguments

x

An object of class mixEM.

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

Label of x-axis to be passed to the loglikelihood plot. Trying to change these parameters using xlab result in an error.

xlab1.size

Font of xlab1.

xtick1.size

Font of tick labels of x-axis to be passed to the loglikelihood plot.

ylab1

Label of y-axis to be passed to the loglikelihood plot. Trying to change these parameters using ylab result in an error.

ylab1.size

Font of ylab1.

ytick1.size

Font of tick labels of y-axis to be passed to the loglikelihood plot.

title1

Title to be passed to the loglikelihood plot.

title1.size

Tile size of the loglikelihood plot.

title1.x

Horizontal position of the loglikelihood plot.

title1.y

Verticle position of the loglikelihood plot.

col1

Color of the loglikelihood plot.

lwd1

Width of the density curve of the loglikelihood plot.

cex1

Dot size of the loglikelihood plot.

xlab2

Label of x-axis to be passed to the density plot. Trying to change these parameters using xlab result in an error.

xlab2.size

Font of xlab2.

xtick2.size

Font of tick labels of x-axis to be passed to the density plot.

ylab2

Label of y-axis to be passed to the density plot. Trying to change these parameters using ylab result in an error.

ylab2.size

Font of ylab2.

ytick2.size

Font of tick labels of y-axis to be passed to the density plot.

title2

Title to be passed to the density plot.

title2.size

Tile size of the density plot.

title2.x

Horizontal position of the density plot.

title2.y

Verticle position of the density plot.

col2

Color of the density plot.

lwd2

Width of the density curve of the density plot.

cex2

Dot size of the density plot.

col.hist

Color of the histogram of 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

If TRUE, marginal density is presented on the side of the corresponding variable.

See Also

post.beta

Examples

if (FALSE) {
## EM output for data generated from a 2-component binary logistic regression model.
beta <- matrix(c(-10, .1, 20, -.1), 2, 2)
x <- runif(500, 50, 250)
x1 <- cbind(1, x)
xbeta <- x1
w <- rbinom(500, 1, .3)
y <- w*rbinom(500, size = 1, prob = (1/(1+exp(-xbeta[, 1]))))+
  (1-w)*rbinom(500, size = 1, prob =
                 (1/(1+exp(-xbeta[, 2]))))
out.2 <- logisregmixEM(y, x, beta = beta, lambda = c(.3, .7),
                       verb = TRUE, epsilon = 1e-01)
plotly_mixEM(out.2 , col2 = c("red" , "green") , density = TRUE)

## Fitting randomly generated data with a 2-component location mixture of bivariate normals.
set.seed(100)
x.1 <- rmvnorm(40, c(0, 0))
x.2 <- rmvnorm(60, c(3, 4))
X.1 <- rbind(x.1, x.2)
mu <- list(c(0, 0), c(3, 4))
out.1 <- mvnormalmixEM(X.1, arbvar = FALSE, mu = mu,
                       epsilon = 1e-02)
plotly_mixEM(out.1 , col2 = c("brown" , "blue") ,
             alpha = c(0.01 , 0.05 , 0.1),
             density = TRUE , marginal = FALSE)

## Fitting randomly generated data with a 2-component scale mixture of bivariate normals.
x.3 <- rmvnorm(40, c(0, 0), sigma =
                 matrix(c(200, 1, 1, 150), 2, 2))
x.4 <- rmvnorm(60, c(0, 0))
X.2 <- rbind(x.3, x.4)
lambda <- c(0.40, 0.60)
sigma <- list(diag(1, 2), matrix(c(200, 1, 1, 150), 2, 2))
out.2 <- mvnormalmixEM(X.2, arbmean = FALSE,
                       sigma = sigma, lambda = lambda,
                       epsilon = 1e-02)
plotly_mixEM(out.1 , col2 = c("brown" , "blue") ,
             alpha = c(0.01 , 0.05 , 0.1),
             density = TRUE , marginal = TRUE)

## EM output for simulated data from 2-component mixture of random effects.
data(RanEffdata)
set.seed(100)
x <- lapply(1:length(RanEffdata), function(i)
  matrix(RanEffdata[[i]][, 2:3], ncol = 2))
x <- x[1:20]
y <- lapply(1:length(RanEffdata), function(i)
  matrix(RanEffdata[[i]][, 1], ncol = 1))
y <- y[1:20]
lambda <- c(0.45, 0.55)
mu <- matrix(c(0, 4, 100, 12), 2, 2)
sigma <- 2
R <- list(diag(1, 2), diag(1, 2))
em.out <- regmixEM.mixed(y, x, sigma = sigma, arb.sigma = FALSE,
                         lambda = lambda, mu = mu, R = R,
                         addintercept.random = FALSE,
                         epsilon = 1e-02, verb = TRUE)
plotly_mixEM(em.out , col2 = c("gold" , "purple") , 
             density = TRUE , lwd2 = 1 , cex2 =9)

## 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)
plotly_mixEM(out, density = TRUE , col2 = c("gold" , "purple"))

## EM output for the water-level task data set.
data(Waterdata)
set.seed(100)
water <- t(as.matrix(Waterdata[,3:10]))
em.out <- repnormmixEM(water, k = 2, verb = TRUE, epsilon = 1e-03)
plotly_mixEM(em.out, density = TRUE , col2 = c("gold" , "purple"))
}