plot.betareg: Diagnostic Plots for betareg Objects

In betareg: Beta Regression

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", 
    "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)

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.
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 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, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. \Sexpr[results=rd]{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.

See Also

betareg

Examples

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))

betareg documentation built on Sept. 13, 2024, 1:07 a.m.