Plot Diagnostic for a glm.cmp Object

Source: R/graphics_related.R

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.

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

Arguments

x

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.

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).

Details

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 \(R_i/\sqrt{1-h_{ii}}\) 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.

See also

compPIT, compnormRandPIT, glm.cmp and autoplot.

Examples

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

## The default plots are shown
plot(M.bids)





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