autoplot.cmp: Plot Diagnostic for a 'glm.cmp' Object in ggplot style
In mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Description Usage Arguments Details Value See Also Examples

autoplot uses ggplot2 to draw the diagnostic plots for a 'cmp' class object. gg_plot is an alias for it.

## S3 method for class 'cmp'
autoplot(
  object,
  which = c(1L, 2L, 6L, 8L),
  bins = 10,
  ask = TRUE,
  nrow = NULL,
  ncol = NULL,
  output_as_ggplot = TRUE,
  ...
)

gg_plot(
  object,
  which = c(1L, 2L, 6L, 8L),
  bins = 10,
  ask = TRUE,
  nrow = NULL,
  ncol = NULL,
  output_as_ggplot = TRUE,
  ...
)

`object`	an object class 'cmp' object, obtained from a call to `glm.cmp`
`which`	if a subset of plots is required, specify a subset of the numbers 1:8. See 'Details' below.
`bins`	numeric; the number of bins shown in the PIT histogram or the PIT Q-Q plot.
`ask`	logical; if `TRUE`, the user is asked before each plot.
`nrow`	numeric; (optional) number of rows in the plot grid.
`ncol`	numeric; (optional) number of columns in the plot grid.
`output_as_ggplot`	logical; if `TRUE`, the function would return a list of `ggplot` objects; if `FALSE`, the function would return an `ggarrange` object.
`...`	other arguments passed to or from other methods (currently unused).

Eight plots (selectable by which) are currently available: a plot of deviance residuals against fitted values, a non-randomized PIT histogram, a uniform Q-Q plot for non-randomized PIT, a histogram of the normal randomized residuals, a Q-Q plot of the normal randomized residuals, a Scale-Location plot of sqrt(| residuals |) against fitted values a plot of Cook's distances versus row labels a plot of pearson residuals against leverage. By default, four plots (number 1, 2, 6, and 8 from this list of plots) are provided.

The 'Scale-Location' plot, also called 'Spread-Location' plot, takes the square root of the absolute standardized deviance residuals (sqrt|E|) in order to diminish skewness is much less skewed than than |E| for Gaussian zero-mean E.

The 'Scale-Location' plot uses the standardized deviance residuals while the Residual-Leverage plot uses the standardized pearson residuals. They are given as R_i/√{1-h_{ii}} where h_{ii} are the diagonal entries of the hat matrix.

The Residuals-Leverage plot shows contours of equal Cook's distance for values of 0.5 and 1.

There are two plots based on the non-randomized probability integral transformation (PIT) using compPIT. These are a histogram and a uniform Q-Q plot. If the model assumption is appropriate, these plots should reflect a sample obtained from a uniform distribution.

There are also two plots based on the normal randomized residuals calculated using compnormRandPIT. These are a histogram and a normal Q-Q plot. If the model assumption is appropriate, these plots should reflect a sample obtained from a normal distribution.

return a list of ggplot objects or a ggarrange object.

compPIT, compnormRandPIT, glm.cmp and plot.cmp.

data(takeoverbids)
M.bids <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght 
    + bidprem + insthold + size + sizesq + regulatn, data=takeoverbids)

## The default plots are shown
gg_plot(M.bids) # or autoplot(M.bids)

## The plots for the non-randomized PIT 
gg_plot(M.bids, which = c(2,3)) # or autoplot(M.bids, which = c(2,3))