Using ggplot histogram instead of hist function in R

Question

I am using a package called BetaMixture in R to fit a mixture of beta distributions for a data vector. The output is supplied to a hist that produces a good histogram with the mixture model components:

# Install and load the libraries

#install.packages("BetaModels")
library(BetaModels)

# Create a vector, fit mixture models and plot the histogram

vec <- c(rbeta(700, 5, 2), rbeta(300, 1, 10))
model <- BetaMixture(vec,2)
h <- hist(model, breaks = 35)

So far so good. Now how do I get this in ggplot? I inspected the h object but that is not different from the model object. They are exactly the same. I don't know how this hist even works for this class. What does it pull from the model to generate this plot other than the @datavec ?

Answer 1

You can get the hist function for BetaMixed objects using getMethod("hist", "BetaMixture") .
Below you can find a simple translation of this function into the " ggplot2 world".

myhist <- function (x, ...) {
    .local <- function (x, mixcols = 1:7, breaks=25, ...) 
    {
        df1 <- data.frame(x=x@datavec)
        p <- ggplot(data=df1, aes(x=x)) + 
             geom_histogram(aes(y=..density..), bins=breaks, alpha=0.5, fill="gray50", color="black")
        while (length(mixcols) < ncol(x@mle)) mixcols <- c(mixcols, 
            mixcols)
        xv <- seq(0, 1, length = 502)[1:501]
        for (J in 1:ncol(x@mle)) {
            y <- x@phi[J] * dbeta(xv, x@mle[1, J], x@mle[2, J])
            df2 <- data.frame(xv, y)
            p <- p + geom_line(data=df2, aes(xv, y), size=1, col=mixcols[J])
        }
        p <- p + theme_bw()
        invisible(p)
    }
    .local(x, ...)
}

library(ggplot2)
# Now p is a ggplot2 object.
p <- myhist(model, breaks=35)
print(p)

Answer 2

The object returned by BetaMixture is a S4 class object with 2 slots that are of interest.

Slot Z returns a matrix of the probabilities of each data point belonging to each of the distributions.
So in the first 6 rows, all points belong to the 2nd distribution.

head(model@Z)
#             [,1]      [,2]
#[1,] 1.354527e-04 0.9998645
#[2,] 4.463074e-03 0.9955369
#[3,] 1.551999e-03 0.9984480
#[4,] 1.642579e-03 0.9983574
#[5,] 1.437047e-09 1.0000000
#[6,] 9.911427e-04 0.9990089

And slot mle return the maximum likelihood estimates of the parameters.

Now use those values to create a data.frame of the vector and a data.frame of the parameters.

df1 <- data.frame(vec)
df1$component <- factor(apply(model@Z, 1, which.max))
colors <- as.integer(levels(df1$component))

params <- as.data.frame(t(model@mle))
names(params) <- c("shape1", "shape2")

Plot the data.

library(ggplot2)

g <- ggplot(df1, aes(x = vec, group = component)) +
  geom_histogram(aes(y = ..density..),
                 bins = 35, fill = "grey", color = "grey40")

for(i in 1:nrow(params)){
  sh1 <- params$shape1[i]
  sh2 <- params$shape2[i]
  g <- g + stat_function(
    fun = dbeta,
    args = list(shape1 = sh1, shape2 = sh2),
    color = colors[i]
  )
}
suppressWarnings(print(g + theme_bw()))