plot_hdpglm | R Documentation |
this function creates a plot with two grids. One is the grid with posterior expectation of betas as function of context-level covariates. The other is the posterior distribution of tau
plot_hdpglm( samples, X = NULL, W = NULL, ncol.taus = 1, ncol.betas = NULL, ncol.w = NULL, nrow.w = NULL, smooth.line = FALSE, pred.pexp.beta = FALSE, title.tau = NULL, true.tau = NULL, title.beta = NULL, tau.x.axis.size = 1.1, tau.y.axis.size = 1.1, tau.title.size = 1.2, tau.panel.title.size = 1.4, tau.legend.size = 1, beta.x.axis.size = 1.1, beta.y.axis.size = 1.1, beta.title.size = 1.2, beta.panel.title.size = 1.4, beta.legend.size = 1, tau.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 |
ncol.taus |
integer with the number of columns of the grid containing the posterior distribution of tau |
ncol.betas |
integer with the number of columns of the posterior expectation of betas as function of context-level features |
ncol.w |
integer with the number of columns to use to display the different context-level covariates |
nrow.w |
integer with the number of rows to use to display the different context-level covariates |
smooth.line |
boolean, if |
pred.pexp.beta |
boolean, if |
title.tau |
string, the title for the posterior distribution of the context effects |
true.tau |
a |
title.beta |
string, the title for the posterior expectation of beta as function of context-level covariate |
tau.x.axis.size |
numeric, relative size of the x-axis of the plot with tau |
tau.y.axis.size |
numeric, relative size of the y-axis of the plot with tau |
tau.title.size |
numeric, relative size of the title of the plot with tau |
tau.panel.title.size |
numeric, relative size of the title of the panels of the plot with tau |
tau.legend.size |
numeric, relative size of the legend of the plot with tau |
beta.x.axis.size |
numeric, relative size of the x-axis of the plot with beta |
beta.y.axis.size |
numeric, relative size of the y-axis of the plot with beta |
beta.title.size |
numeric, relative size of the title of the plot with beta |
beta.panel.title.size |
numeric, relative size of the title of the panels of the plot with beta |
beta.legend.size |
numeric, relative size of the legend of the plot with beta |
tau.xlab |
string, the label of the x-axis for the plot with tau |
library(magrittr) # 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_hdpglm(samples) plot_hdpglm(samples, ncol.taus=2, ncol.betas=2, X='x1') plot_hdpglm(samples, ncol.taus=2, ncol.betas=2, X='x1', ncol.w=2, nrow.w=1, pred.pexp.beta=TRUE,smooth.line=TRUE )
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