# Plot

Source: `R/src_summaries.R`
`plot.hdpGLM.Rd`

Generic function to plot the posterior density estimation produced by the function `hdpGLM`

    # S3 method for hdpGLM
    plot(
      x,
      terms = NULL,
      j.label = NULL,
      j.idx = NULL,
      title = NULL,
      subtitle = NULL,
      true.beta = NULL,
      ncol = NULL,
      legend.position = "bottom",
      display.terms = TRUE,
      context.id = NULL,
      ylab = NULL,
      xlab = NULL,
      x.axis.size = 1.1,
      y.axis.size = 1.1,
      title.size = 1.2,
      panel.title.size = 1.5,
      legend.size = 1.1,
      rel.height = 0.01,
      fill.col = "#00000044",
      border.col = "white",
      ...
    )

## Arguments

x

  an object of the class `hdpGLM` generted by the function hdpGLM

terms

  string vector with the name of the individual-level covariates to plot. If `NULL` (default), all covariates are plotted.

j.label

  string vector with the names of the contexts to plot. An alternative is to use the context indexes with the parameter `j.idx` instead of the context labels. If `NULL` (default) and j.idx is also `NULL`, the posterior distribution of all contexts are plotted. Note: if contexts to plot are selected using `j.label`, the parameter `context.id` must also be provided.

j.idx

  integer vector with the index of the contexts to plot. An alternative is to use the context labels with the parameter `j.label` instead of the indexes. If `NULL` (default) and j.label is also `NULL`, the posterior distribution of all contexts are plotted

title

  string, the title of the plot

subtitle

  string, the subtitle of the plot

true.beta

  a `data.frame` with the true values of the linear coefficients `beta` if they are known. The `data.frame` must contain a column named `j` with the index of the context associated with that particular linear coefficient `beta`. It must match the indexes used in the data set for each context. Another column named `k` must be provided, indicating the cluster of `beta`, and a column named `Parameter` with the name of the linear coefficients (`beta1`, `beta2`, ..., `beta_dx`, where `dx` is the number of covariates at the individual level, and beta1 is the coefficient of the intercept term). It must contain a column named `True` with the true value of the `betas`. Finally, the `data.frame` must contain columns with the context-level covariates as used in the estimation of the hdpGLM function (see Details below).

ncol

  interger, the number of columns in the plot

legend.position

  one of four options: "bottom" (default), "top", "left", or "right". It indicates the position of the legend

display.terms

  boolean, if `TRUE` (default), the covariate name is displayed in the plot

context.id

  string with the name of the column containing the labels identifying the contexts. This variable should have been specified when the estimation was conducted using the function `hdpGLM`.

ylab

  string, the label of the y-axis

xlab

  string, the label of the x-axis

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, the relative size of the title of the plot

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

rel.height

  see ggridges::geom_density_ridges

fill.col

  string with the color of the densities

border.col

  string with the color of the border of the densities

...

  Additional arguments accepted are:

  `true.beta`: a `data.frame` with the true values of the linear coefficients `beta` if they are known. The `data.frame` must contain a column named `j` with the index of the context associated with that particular linear coefficient `beta`. It must match the indexes used in the data set for each context. Another column named `k` must be provided, indicating the cluster of `beta`, and a column named `Parameter` with the name of the linear coefficients (`beta1`, `beta2`, ..., `beta_dx`, where `dx` is the number of covariates at the individual level, and beta1 is the coefficient of the intercept term). It must contain a column named `True` with the true value of the `betas`. Finally, the `data.frame` must contain columns with the context-level covariates as used in the estimation of the hdpGLM function (see Details below).

  `true.tau`: a `data.frame` with four columns. The first must be named `w` and it indicates the index of each context-level covariate, starting with 0 for the intercept term. The second column named `beta` must contain the indexes of the betas of individual-level covariates, starting with 0 for the intercept term. The third column named `Parameter` must be named `tau<w><beta>`, where `w` and `beta` must be the actual values displayed in the columns `w` and `beta`. Finally, it must have a column named `True` with the true value of the parameter.