Various types of standard diagnostic plots can be produced, involving various types of residuals, influence measures etc.
# S3 method for 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",
"Half-normal 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)
fitted model object of class "betareg"
.
numeric. If a subset of the plots is required, specify a subset of the numbers 1:6
.
character. Captions to appear above the plots.
character. Common title-above figures if there are multiple.
character. Title to each plot in addition to the above caption
.
logical. If TRUE
, the user is asked before each plot.
other parameters to be passed through to plotting functions.
character indicating type of residual to be used, see residuals.betareg
.
numeric. Number of simulations in half-normal plots.
numeric. Confidence level in half-normal plots.
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 Cribari-Neto (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
).
Half-normal plot of residuals (which = 5
), which is obtained using a simulation
approach. Predicted vs observed values (which = 6
).
Cribari-Neto F, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1--24. tools:::Rd_expr_doi("10.18637/jss.v034.i02")
Ferrari SLP, Cribari-Neto F (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799--815.
betareg
data("GasolineYield", package = "betareg")
gy <- betareg(yield ~ gravity + pressure + temp10 + temp, data = GasolineYield)
par(mfrow = c(3, 2))
plot(gy, which = 1:6)
par(mfrow = c(1, 1))
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