plot.dpGLM: Default plot for class dpGLM
In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

This function generates desity plots with the posterior distribution generated by the function hdpGLM

## S3 method for class 'dpGLM'
plot(
  x,
  terms = NULL,
  separate = FALSE,
  hpd = TRUE,
  true.beta = NULL,
  title = NULL,
  subtitle = NULL,
  adjust = 1,
  ncols = NULL,
  only.occupied.clusters = TRUE,
  focus.hpd = FALSE,
  legend.position = "top",
  colour = "grey",
  alpha = 0.4,
  display.terms = TRUE,
  plot.mean = TRUE,
  legend.label.true.value = "True",
  ...
)

`x`	a dpGLM object with the samples from generated by `hdpGLM`
`terms`	string vector with the name of covariates to plot. If `NULL` (default), all covariates are plotted.
`separate`	boolean, if `TRUE` the linear coefficients `beta` will be displayed in their separate clusters.
`hpd`	boolean, if `TRUE` and `separate=T`, the 95% HPDI lines will be displayed.
`true.beta`	either `NULL` (default) or a `data.frame` with the true values of the linear coefficients `beta` if they are known. The `data.frame` must contain a column named `k` indicating the cluster of `beta`, and a column named `Parameter` with the name of the linear coefficients (`beta1`, `beta2`, ..., `beta_dx`, where `dx` is the number of covariates at the individual level, and beta1 is the coefficient of the intercept term). It must contain a column named `True` with the true value of the `betas`.
`title`	string, the title of the plot
`subtitle`	string, the subtitle of the plot
`adjust`	the bandwidth used is actually `adjust*bw`. This makes it easy to specify values like ‘half the default’ bandwidth.
`ncols`	integer, the number of columns in the plot
`only.occupied.clusters`	boolean, if `TRUE` it shows only the densities of the clusters that actually have data points assigned to it with high probability
`focus.hpd`	boolean, if `TRUE` and separate is also `TRUE` it will display only the 95% HPDI of the posterior density of the linear coefficients `beta`
`legend.position`	one of four options: "bottom" (default), "top", "left", or "right". It indicates the position of the legend
`colour`	= string with color to fill the density plot
`alpha`	number between 0 and 1 indicating the degree of transparency of the density
`display.terms`	boolean, if `TRUE` (default), the covariate name is displayed in the plot
`plot.mean`	boolean, if `TRUE` the posterior mean of every cluster is displayed
`legend.label.true.value`	a string with the value to display in the legend when the `true.beta` is used
`...`	ignored

# Note: this example is just for illustration. MCMC iterations are very reduced
set.seed(10)
n = 20
data = tibble::data_frame(x1 = rnorm(n, -3),
                                   x2 = rnorm(n,  3),
                                   z  = sample(1:3, n, replace=TRUE),
                                   y  =I(z==1) * (3 + 4*x1 - x2 + rnorm(n)) +
                                       I(z==2) * (3 + 2*x1 + x2 + rnorm(n)) +
                                       I(z==3) * (3 - 4*x1 - x2 + rnorm(n)) ,
                                   ) 

## estimation
mcmc    = list(burn.in=1, n.iter=50)
samples = hdpGLM(y ~ x1 + x2,  data=data, mcmc=mcmc, n.display=1)

plot(samples)