plot.betareg {betareg} | R Documentation |
Various types of standard diagnostic plots can be produced, involving various types of residuals, influence measures etc. Half-normal plots can be produced using a simulation approach.
## 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 = "sweighted2", nsim = 100, level = 0.9)
x |
fitted model object of class "betareg" . |
which |
if a subset of the plots is required, specify a subset of the numbers 1:8 . |
caption |
captions to appear above the plots. |
sub.caption |
common title-above figures if there are multiple. |
main |
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 |
number of simulations in half-normal plots. |
level |
confidence level in half-normal plots. |
Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
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))