izip_graphics: Plot Diagnostic for a 'glm.izip' Object
In thomas-fung/izipr: Interpretable Zero-Inflated Poisson Models in R

Description Usage Arguments Details Value See Also Examples

Eight plots (selectable by which) are currently available using ggplot or graphics: 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.

## S3 method for class 'izip'
plot(
  x,
  which = c(1L, 2L, 6L, 8L),
  ask = prod(par("mfcol")) < length(which) && dev.interactive(),
  bins = 10,
  ...
)

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

`x`	an object class 'izip' object, obtained from a call to `glm.izip`
`which`	if a subset of plots is required, specify a subset of the numbers 1:8. See 'Details' below.
`ask`	logical; if `TRUE`, the user is asked before each plot.
`bins`	numeric; the number of bins shown in the PIT histogram or the PIT Q-Q plot.
`...`	other arguments passed to or from other methods (currently unused).
`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, which is a ggplot or a list of ggplot.

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 izipPIT. 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 izipnormRandPIT. 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.

A ggarrange object, which is a ggplot or a list of ggplot for autoplot.

izipPIT, izipnormRandPIT, and glm.izip.

data(bioChemists)
M_bioChem <- glm.izip(art ~ ., data = bioChemists)

## The default plots are shown
plot(M_bioChem) # or autoplot(M_bioChem)

## The plots for the non-randomized PIT
plot(M_bioChem, which = c(2, 3))
# or autoplot(M_bioChem, which = c(2,3))