hdpGLM (version 1.0.4)

plot_pexp_beta: 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)