plot_tau: Plot tau
In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

Function to plot posterior distribution of tau

plot_tau(
  samples,
  X = NULL,
  W = NULL,
  title = NULL,
  true.tau = NULL,
  show.all.taus = FALSE,
  show.all.betas = FALSE,
  ncol = NULL,
  legend.position = "top",
  x.axis.size = 1.1,
  y.axis.size = 1.1,
  title.size = 1.2,
  panel.title.size = 1.4,
  legend.size = 1,
  xlab = NULL
)

`samples`	an output of the function `hdpGLM`
`X`	a string vector with the name of the first-level covariates whose associated tau should be displayed
`W`	a string vector with the name of the context-level covariate(s) whose linear effect will be displayed. If `NULL`, the linear effect tau of all context-level covariates are displayed. Note: the context-level covariate must have been included in the estimation of the model.
`title`	string, title of the plot
`true.tau`	a `data.frame` with four columns. The first must be named `w` and it indicates the index of each context-level covariate, starting with 0 for the intercept term. The second column named `beta` must contain the indexes of the betas of individual-level covariates, starting with 0 for the intercept term. The third column named `Parameter` must be named `tau<w><beta>`, where `w` and `beta` must be the actual values displayed in the columns `w` and `beta`. Finally, it must have a column named `True` with the true value of the parameter.
`show.all.taus`	boolean, if `FALSE` (default) the posterior distribution of taus representing the intercept of the expectation of beta are omitted
`show.all.betas`	boolean, if `FALSE` (default) the taus affecting only the intercept terms of the outcome variable are omitted
`ncol`	number of columns of the grid. If `NULL`, one column is used
`legend.position`	one of four options: "bottom" (default), "top", "left", or "right". It indicates the position of the legend
`x.axis.size`	numeric, the relative size of the label in the x-axis
`y.axis.size`	numeric, the relative size of the label in the y-axis
`title.size`	numeric, the relative size of the title of the plot
`panel.title.size`	numeric, the relative size of the titles in the panel of the plot
`legend.size`	numeric, the relative size of the legend
`xlab`	string, the label of the x-axis

library(magrittr)
set.seed(66)

# Note: this example is just for illustration. MCMC iterations are very reduced
set.seed(10)
n = 20
data.context1 = 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)) ,
                                   w = 20
                                   ) 
data.context2 = tibble::data_frame(x1 = rnorm(n, -3),
                                   x2 = rnorm(n,  3),
                                   z  = sample(1:2, n, replace=TRUE),
                                   y  =I(z==1) * (1 + 3*x1 - 2*x2 + rnorm(n)) +
                                       I(z==2) * (1 - 2*x1 +   x2 + rnorm(n)),
                                   w = 10
                                   ) 
data = data.context1 %>%
    dplyr::bind_rows(data.context2)

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


plot_tau(samples)
plot_tau(samples, ncol=2)
plot_tau(samples, X='x1', W='w')
plot_tau(samples, show.all.taus=TRUE, show.all.betas=TRUE, ncol=2)