| plot_tau | R Documentation | 
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  | 
| 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  | 
| title | string, title of the plot | 
| true.tau | a  | 
| show.all.taus | boolean, if  | 
| show.all.betas | boolean, if  | 
| ncol | number of columns of the grid. If  | 
| 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::tibble(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::tibble(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)
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