plot_pexp_beta: Plot beta posterior expectation

In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

Plot beta posterior expectation

Description

This function plots the posterior expectation of beta, the linear effect of the individual level covariates, as function of the context-level covariates

Usage

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
)

Arguments

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.
pred.pexp.beta	boolean, if TRUE the plots will display a line with the predicted posterior expectation of betas obtained using the posterior expectation of taus, the linear coefficients of the expectation of beta
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 TRUE the plot will display a regression line representing the regression of the posterior expectation of the linear coefficients betas on the context-level covariates. Default FALSE
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 pred.pexp.beta=TRUE
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

Examples

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)

hdpGLM documentation built on May 7, 2022, 1:06 a.m.