plot_pexp_beta: Plot posterior expectation of beta in each context
In hdpGLM: Hierarchical Dirichlet Process Generalized Linear Models

Description Usage Arguments Examples

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 `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

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::data_frame(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::data_frame(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)