Description Usage Arguments Details Author(s) References See Also Examples

Plot the fitted values vs the studentized or standardized residuals for a `glm`

or `lm`

object.

1 2 |

`model` |
a regression model with any number of predictors. Must be a |

`zoom` |
what range of residuals you wish to show in your plot. By default, zoom is |

`highlight.outliers` |
logical. If |

`residuals` |
which type of residuals to use. Studentized residuals are used by default, but can be specified with |

A residual plot shows the fitted values of the response variable on the x-axis and the studentized or standardized residuals on the y-axis. It can be used to check for correlated residuals or non-constant variance of the residuals, both of which would violate the residual assumptions of a linear model. It can also be used to check for outliers, as a value below -3 or above 3 would indicate a residual which is more than 3 standard deviations from the mean of 0.

Jonathan Schwartz

Montgomery, D. C., Peck, E. A., Vining, G. G. (2013), Introduction to Linear Regression Analysis, Hoboken, NJ: John Wiley & Sons, Inc.

`plot`

,
`abline`

,
`lm`

,
`glm`

,
`predict`

,
`rstudent`

,
`rstandard`

1 2 3 4 5 6 7 8 9 10 | ```
##plot a residual plot to check the model assumptions for a linear
##model of iris petal length as a predicted by iris petal width
model<-lm(iris$Petal.Length~iris$Petal.Width)
resplot(model)
##highlight the one outlier
resplot(model,highlight.outliers=TRUE)
##zoom in to only show the residuals between -1 and 1
resplot(model,zoom=1)
``` |

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