plotly_mixEM: Visualization of output of 'mixEM' function using 'plotly'

In mixtools: Tools for Analyzing Finite Mixture Models

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)

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.

Value

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

See Also

post.beta

Examples

## Not run: 
## 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"))

## End(Not run)

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