library("betareg")
data("GasolineYield", package = "betareg")
gy < betareg(yield ~ gravity + pressure + temp10 + temp, data = GasolineYield)
par(mfrow = c(3, 2))
plot(gy, which = 1:6)
Diagnostic Plots for betareg Objects
Description
Various types of standard diagnostic plots can be produced, involving various types of residuals, influence measures etc.
Usage
## S3 method for class 'betareg'
plot(x, which = 1:4,
caption = c("Residuals vs indices of obs.", "Cook's distance plot",
"Generalized leverage vs predicted values", "Residuals vs linear predictor",
"Halfnormal plot of residuals", "Predicted vs observed values"),
sub.caption = paste(deparse(x\$call), collapse = "\n"), main = "",
ask = prod(par("mfcol")) < length(which) && dev.interactive(),
..., type = "quantile", nsim = 100, level = 0.9)
Arguments
x

fitted model object of class “betareg” .

which

numeric. If a subset of the plots is required, specify a subset of the numbers 1:6 .

caption

character. Captions to appear above the plots. 
sub.caption

character. Common titleabove figures if there are multiple. 
main

character. Title to each plot in addition to the above caption .

ask

logical. If TRUE , the user is asked before each plot.

…

other parameters to be passed through to plotting functions. 
type

character indicating type of residual to be used, see residuals.betareg .

nsim

numeric. Number of simulations in halfnormal plots. 
level

numeric. Confidence level in halfnormal plots. 
Details
The plot
method for betareg
objects produces various types of diagnostic plots. Most of these are standard for regression models and involve various types of residuals, influence measures etc. See Ferrari and CribariNeto (2004) for a discussion of some of these displays.
The which
argument can be used to select a subset of currently six supported types of displays. The corresponding element of caption
contains a brief description. In some more detail, the displays are: Residuals (as selected by type
) vs indices of observations (which = 1
). Cook’s distances vs indices of observations (which = 2
). Generalized leverage vs predicted values (which = 3
). Residuals vs linear predictor (which = 4
). Halfnormal plot of residuals (which = 5
), which is obtained using a simulation approach. Predicted vs observed values (which = 6
).
References
CribariNeto F, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. doi:10.18637/jss.v034.i02
Ferrari SLP, CribariNeto F (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
See Also
betareg