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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