plot.betareg {betareg} | R Documentation |

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

## 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 |

`which` |
numeric. If a subset of the plots is required, specify a subset of the numbers |

`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 |

`ask` |
logical. If |

`...` |
other parameters to be passed through to plotting functions. |

`type` |
character indicating type of residual to be used, see |

`nsim` |
numeric. Number of simulations in half-normal plots. |

`level` |
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., and Zeileis, A. (2010). Beta Regression in R.
*Journal of Statistical Software*, **34**(2), 1–24.
doi: 10.18637/jss.v034.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.

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))

[Package *betareg* version 3.1-4 Index]