0th

Percentile

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':
"by", varnames[-response]), ...)

## S3 method for class 'default':
"by", deparse(substitute(by))), col=palette()[1], col.lines=palette()[2],
pch=1, lwd=2, grid=TRUE, ...)

## S3 method for class 'lm':
xlab="Fitted Values",
ylab="Absolute Studentized Residuals", las=par("las"),
pch=1, col=palette()[1], col.lines=palette()[2], lwd=2, grid=TRUE, ...)

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

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
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 NAs.
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 color palette (see palette
and par).}
col.lines{color for lines; default is the second entry in the current
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}
...{arguments passed to plotting functions.}
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).

The function slp is an abbreviation for spreadLevelPlot.
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.}
Fox, J. (2008)
Applied Regression Analysis and Generalized Linear Models,
Second Edition. Sage.

Fox, J. and Weisberg, S. (2011)
An R Companion to Applied Regression, Second Edition, Sage.

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

hccm, ncvTest

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

hplot
regression