spreadLevelPlot {car} | R Documentation

## Spread-Level Plots

### Description

Creates plots for examining the possible dependence of spread on level, or an extension of these plots to the studentized residuals from linear models.

### Usage

    spreadLevelPlot(x, ...)

    slp(...)

    ## S3 method for class 'formula'
    spreadLevelPlot(x, data=NULL, subset, na.action,
        main=paste("Spread-Level Plot for", varnames[response],
        "by", varnames[-response]), ...)

    ## Default S3 method:
    spreadLevelPlot(x, by, robust.line=TRUE,
    	start=0, xlab="Median", ylab="Hinge-Spread",
    	point.labels=TRUE, las=par("las"),
    	main=paste("Spread-Level Plot for", deparse(substitute(x)),
    		"by", deparse(substitute(by))),
    	col=carPalette()[1], col.lines=carPalette()[2],
        pch=1, lwd=2, grid=TRUE, ...)

    ## S3 method for class 'lm'
    spreadLevelPlot(x, robust.line=TRUE,
    	xlab="Fitted Values", ylab="Absolute Studentized Residuals", las=par("las"),
    	main=paste("Spread-Level Plot for\n", deparse(substitute(x))),
    	pch=1, col=carPalette()[1], col.lines=carPalette()[2:3], lwd=2, grid=TRUE,
        id=FALSE, smooth=TRUE, ...)

    ## S3 method for class 'spreadLevelPlot'
    print(x, ...)

### Arguments

`x` | a formula of the form `y ~ x`, where `y` is a numeric vector and `x` is a factor, or an `lm` object to be plotted; alternatively a numeric vector.
`data` | an optional data frame containing the variables to be plotted. By default the variables are taken from the environment from which `spreadLevelPlot` is called.
`subset` | an optional vector specifying a subset of observations to be used.
`na.action` | a function that indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`.
`by` | a factor, numeric vector, or character vector defining groups.
`robust.line` | if `TRUE` a robust line is fit using the `rlm` function in the `MASS` package; if `FALSE` a line is fit using `lm`.
`start` | add the constant `start` to each data value.
`main` | title for the plot.
`xlab` | label for horizontal axis.
`ylab` | label for vertical axis.
`point.labels` | if `TRUE` label the points in the plot with group names.
`las` | if `0`, ticks labels are drawn parallel to the axis; set to `1` for horizontal labels (see `par`).
`col` | color for points; the default is the first entry in the current car palette (see `carPalette` and `par`).
`col.lines` | for the default method, the line color, defaulting to the second entry in the car color palette; for the `"lm"` method, a vector of two colors for, respectively, the fitted straight line and a nonparametric regression smooth line, default to the second and third entries in the car color palette.
`pch` | plotting character for points; default is `1` (a circle, see `par`).
`lwd` | line width; default is `2` (see `par`).
`grid` | If TRUE, the default, a light-gray background grid is put on the graph

`id`

controls point identification; if `FALSE` (the default), no points are identified; can be a list of named arguments to the `showLabels` function; `TRUE` is equivalent to `list(method=list("x", "y"), n=2, cex=1, col=carPalette()[1], location="lr")`, which identifies the 2 points the most extreme horizontal ("X", absolute studentized residual) values and the 2 points with the most extreme horizontal ("Y", fitted values) values.

`smooth` | specifies the smoother to be used along with its arguments; if `FALSE`, no smoother is shown; can be a list giving the smoother function and its named arguments; `TRUE`, the default, is equivalent to `list(smoother=loessLine)`. See `ScatterplotSmoothers` for the smoothers supplied by the car package and their arguments.
`...` | arguments passed to plotting functions.

### Details

Except for linear models, computes the statistics for, and plots, a Tukey spread-level plot of log(hinge-spread) vs. log(median) for the groups; fits a line to the plot; and calculates a spread-stabilizing transformation from the slope of the line.

For linear models, plots log(abs(studentized residuals) vs. log(fitted values). Point labeling was added in November, 2016.

The function `slp` is an abbreviation for `spreadLevelPlot`.

### Value

An object of class `spreadLevelPlot` containing:

`Statistics` | a matrix with the lower-hinge, median, upper-hinge, and hinge-spread for each group. (Not for an `lm` object.)
`PowerTransformation` | spread-stabilizing power transformation, calculated as 1 - slope of the line fit to the plot.

### Author(s)

John Fox jfox@mcmaster.ca

### References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Hoaglin, D. C., Mosteller, F. and Tukey, J. W. (Eds.) (1983) Understanding Robust and Exploratory Data Analysis. Wiley.

### See Also

`hccm`, `ncvTest`

### Examples

    spreadLevelPlot(interlocks + 1 ~ nation, data=Ornstein)
    slp(lm(interlocks + 1 ~ assets + sector + nation, data=Ornstein))

[Package car version 3.0-0 Index]