Plot model summary

Usage

# S3 method for class 'inlamemi'
plot(
  x,
  plot_moi = TRUE,
  plot_imp = TRUE,
  plot_mis = TRUE,
  plot_intercepts = TRUE,
  error_variable_highlight = FALSE,
  greek = FALSE,
  palette = NULL,
  ...
)

Arguments

x: the model returned from the fit_inlamemi function.
plot_moi: should the posterior mean for the coefficients of the model of interest be plotted? Defaults to TRUE.
plot_imp: should the posterior mean for the coefficients of the imputation model be plotted? Defaults to TRUE.
plot_mis: should the posterior mean for the coefficients of the missingness model be plotted? Defaults to TRUE.
plot_intercepts: should the posterior mean for the intercept(s) be plotted? Defaults to TRUE.
error_variable_highlight: should the coefficient(s) of the variable(s) with error be highlighted? (circled in black) Defaults to FALSE.
greek: make the coefficient names into greek letters with the covariate name as subscript. Defaults to FALSE.
palette: either a number (between 1 and 5), indicating the number of the color palette to be used, or a vector of the colors to be used.
...: other arguments

Value

An object of class "ggplot2" that plots the posterior mean and 95 % credible interval for each coefficient in the model. The coefficients are colored to indicate if they belong to the main or imputation model, and the variable with error is also highlighted.

Examples

# Fit the model
simple_model <- fit_inlamemi(data = simple_data,
                           formula_moi = y ~ x + z,
                           formula_imp = x ~ z,
                           family_moi = "gaussian",
                           error_type = c("berkson", "classical"),
                           prior.prec.moi = c(10, 9),
                           prior.prec.berkson = c(10, 9),
                           prior.prec.classical = c(10, 9),
                           prior.prec.imp = c(10, 9),
                           prior.beta.error = c(0, 1/1000),
                           initial.prec.moi = 1,
                           initial.prec.berkson = 1,
                           initial.prec.classical = 1,
                           initial.prec.imp = 1)

plot(simple_model)