diagnostic.plots: Plot Nonparametric Regression Diagnostics
In npreg: Nonparametric Regression via Smoothing Splines

diagnostic.plots

R Documentation

Plot Nonparametric Regression Diagnostics

Description

Six regression diagnostic plots for a fit smoothing spline (fit by ss), smooth model (fit by sm), or generalized smooth model (fit by gsm).

Usage

diagnostic.plots(x, which = c(1, 2, 3, 5), 
           caption = list("Residuals vs Fitted", 
                          "Normal Q-Q", "Scale-Location", 
                          "Cook's distance", "Residuals vs Leverage", 
                          "Cook's dist vs Variance ratio"), 
           panel = if (add.smooth) function(x, y, ...) 
                      panel.smooth(x, y, iter = iter.smooth, ...) 
                   else points, 
           sub.caption = NULL, main = "", 
           ask = prod(par("mfcol")) < length(which) && dev.interactive(), 
           ..., id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75, cex.pt = 1,
           qqline = TRUE, cook.levels = c(0.5, 1), add.smooth = getOption("add.smooth"), 
           iter.smooth = if (isGlm) 0 else 3, label.pos = c(4, 2), cex.caption = 1, 
           cex.oma.main = 1.25, cex.lab = 1, line.lab = 3, xlim = NULL, ylim = NULL)

Arguments

`x`	an object of class "gsm" output by the `gsm` function, "sm" output by the `sm` function, or "ss" output by the `ss` function
`which`	subset of the integers `1:6` indicating which plots to produce
`caption`	captions to appear above the plots
`panel`	panel function (panel.smooth or points?)
`sub.caption`	common title (for use above multiple figures)
`main`	title to each plot (in addition to `caption`)
`ask`	if `TRUE`, the user is asked before each plot
`...`	other parameters to be passed through to plotting functions
`id.n`	number of points to be labeled in each plot, starting with the most extreme
`labels.id`	vector of labels for extreme observations (`NULL` uses the observation numbers)
`cex.id`	magnification of point labels
`cex.pt`	magnification of points
`qqline`	logical indicating if a `qqline` should be added to the normal Q-Q plot
`cook.levels`	levels of Cook's distance at which to draw contours
`add.smooth`	logical indicating if a smoother should be added to most plots
`iter.smooth`	the number of robustness iterations, the argument `iter` in `panel.smooth`
`label.pos`	positioning of the labels, for the left hald and right half of the graph respectively, for plots 1-3, 5, and 6
`cex.caption`	controls the size of the `caption`
`cex.oma.main`	controls the size of the `sub.caption` only if that is above the figures (when there is more than one figure)
`cex.lab`	character expansion factor for axis labels
`line.lab`	on which margin line should the axis labels be drawn?
`xlim`	Limits for x-axis. If `length(which) == 1`, a vector of the form `c(xmin, xmax)`. Otherwise a list the same length as `which` such that each list entry gives the x-axis limits for the corresponding plot.
`ylim`	Limits for y-axis. If `length(which) == 1`, a vector of the form `c(ymin, ymax)`. Otherwise a list the same length as `which` such that each list entry gives the y-axis limits for the corresponding plot.

Details

This function is modeled after the plot.lm function. The structure of the arguments, as well as the internal codes, mimics the plot.lm function whenever possible. By default, only plots 1-3 and 5 are provided, but any subset of plots can be requested using the which argument.

The six plots include: (1) residuals versus fitted values, (2) normal Q-Q plot, (3) scale-location plot of √{|residuals|} versus fitted values, (4) Cook's distances, (5) residuals versus leverages, and (6) Cook's distance versus variance ratio = leverage/(1-leverage).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression. London: Chapman and Hall.

McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. London: Chapman and Hall.

Examples

# generate data
set.seed(1)
n <- 100
x <- seq(0, 1, length.out = n)
fx <- 2 + 3 * x + sin(2 * pi * x)
y <- fx + rnorm(n, sd = 0.5)

# smoothing spline
mod.ss <- ss(x, y, nknots = 10)
diagnostic.plots(mod.ss)

# smooth model
mod.sm <- sm(y ~ x, knots = 10)
diagnostic.plots(mod.sm)

# generalized smooth model (family = gaussian)
mod.gsm <- gsm(y ~ x, knots = 10)
diagnostic.plots(mod.gsm)

npreg documentation built on July 21, 2022, 1:06 a.m.