R: Residual Plot

resplot

R Documentation

Residual Plot

Description

Plot the fitted values vs the studentized or standardized residuals for a glm or lm object.

Usage

resplot(model, zoom = NULL, highlight.outliers = FALSE, 
  residuals = c("student","standard"))

Arguments

`model`	a regression model with any number of predictors. Must be a `glm` or `lm` object.
`zoom`	what range of residuals you wish to show in your plot. By default, zoom is `NULL`, and the residual plot will show all residuals. If you set zoom to a numeric value > 0, resplot will only show residuals which are at most that many standard deviations away from 0.
`highlight.outliers`	logical. If `FALSE` (the default), outliers will not be highlighted. If `TRUE`, every residual which is more than 3 standard deviations from 0 will be circled in red.
`residuals`	which type of residuals to use. Studentized residuals are used by default, but can be specified with `"student"`, `"rstudent"`, or `"studentized"`. Standardized residuals can be specified with `"standard"`, `"rstandard"`, or `"standardized"`

Details

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.

Author(s)

Jonathan Schwartz

References

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

Examples

##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)