plot.betareg

0th

Percentile

Diagnostic Plots for betareg Objects

Various types of standard diagnostic plots can be produced, involving various types of residuals, influence measures etc.

Keywords
regression
Usage
# 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 = "sweighted2", 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 title-above figures if there are multiple.

main

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.

type

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

nsim

numeric. Number of simulations in half-normal plots.

level

numeric. Confidence level in half-normal 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 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).

References

Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1--24. http://www.jstatsoft.org/v34/i02/.

Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799--815.

betareg

• plot.betareg
Examples
# NOT RUN {
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))
# }

Documentation reproduced from package betareg, version 3.1-3, License: GPL-2 | GPL-3

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