plot_pexp_beta | R Documentation |
This function plots the posterior expectation of beta, the linear effect of the individual level covariates, as function of the context-level covariates
plot_pexp_beta( samples, X = NULL, W = NULL, pred.pexp.beta = FALSE, ncol.beta = NULL, ylab = NULL, nrow.w = NULL, ncol.w = NULL, smooth.line = FALSE, title = NULL, legend.position = "top", col.pred.line = "red", x.axis.size = 1.1, y.axis.size = 1.1, title.size = 12, panel.title.size = 1.4, legend.size = 1 )
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 |
pred.pexp.beta |
boolean, if |
ncol.beta |
integer with number of columns of the grid used for each group of context-level covariates |
ylab |
string, the label of the y-axis |
nrow.w |
integer with the number of rows of the grid |
ncol.w |
integer with the number of columns of the grid |
smooth.line |
boolean, if |
title |
string, title of the plot |
legend.position |
one of four options: "bottom" (default), "top", "left", or "right". It indicates the position of the legend |
col.pred.line |
string with color of fitted line. Only works if |
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, absolute size of the title |
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 |
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_pexp_beta(samples) plot_pexp_beta(samples, X='x1', ncol.w=2, nrow.w=1) plot_pexp_beta(samples, X='x1', ncol.beta=2) plot_pexp_beta(samples, pred.pexp.beta=TRUE, W="w", X=c("x1", "x2")) plot_pexp_beta(samples, W='w', smooth.line=TRUE, pred.pexp.beta=TRUE, ncol.beta=2)
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