Plotting fitted models

Generic function for plotting predictions from various types of fitted models. plotFit currently supports objects of class stats::lm(), stats::glm(), and stats::nls(). A default method also exists which may be used for plotting the fitted mean response from other model fits (e.g., MASS::lqs() and MASS::rlm() from the MASS package).

Usage

plotFit(object, ...)

# Default S3 method
plotFit(
  object,
  type = c("response", "link"),
  interval = c("none", "both", "confidence", "prediction"),
  level = 0.95,
  data,
  adjust = c("none", "Bonferroni", "Scheffe"),
  k,
  ...,
  shade = FALSE,
  extend.range = FALSE,
  hide = TRUE,
  col.conf = if (shade) grDevices::grey(0.7) else "black",
  col.pred = if (shade) grDevices::grey(0.9) else "black",
  border.conf = col.conf,
  border.pred = col.pred,
  col.fit = "black",
  lty.conf = if (shade) 1 else 2,
  lty.pred = if (shade) 1 else 3,
  lty.fit = 1,
  lwd.conf = 1,
  lwd.pred = 1,
  lwd.fit = 1,
  n = 500,
  xlab,
  ylab,
  xlim,
  ylim
)

Arguments

object: A fitted model object. Typically, an object that inherits from class stats::lm(), stats::glm(), or stats::nls() (but others may work too).
...: Additional optional arguments passed on to plot().
type: The type of prediction required. The default is on the scale of the response variable; the alternative "link" is on the scale of the linear predictor. This option is only used when plotting stats::glm() objects.
interval: A character string indicating if a prediction band, confidence band, both, or none should be plotted.
level: The desired confidence level.
data: An optional data frame containing the variables in the model.
adjust: A character string indicating the type of adjustment (if any) to make to the confidence/prediction bands.
k: An integer to be used in computing the critical value for the confidence/prediction bands. Only needed when adjust = "Bonferroni", or when adjust = "Scheffe" and interval = "prediction".
shade: A logical value indicating if the band should be shaded.
extend.range: A logical value indicating if the fitted regression line and bands (if any) should extend to the edges of the plot. Default is FALSE.
hide: A logical value indicating if the fitted model should be plotted on top of the points (FALSE) or behind them (TRUE). Default is TRUE.
col.conf: Shade color for confidence band.
col.pred: Shade color for prediction band.
border.conf: The color to use for the confidence band border.
border.pred: The color to use for the prediction band border.
col.fit: The color to use for the fitted line.
lty.conf: Line type to use for confidence band border.
lty.pred: Line type to use for prediction band border.
lty.fit: Line type to use for the fitted regression line.
lwd.conf: Line width to use for confidence band border.
lwd.pred: Line width to use for prediction band border.
lwd.fit: Line width to use for the fitted regression line.
n: The number of predictor values at which to evaluate the fitted model (larger gives a smoother plot).
xlab: A title for the x axis.
ylab: A title for the y axis.
xlim: The x limits (x1, x2) of the plot.
ylim: The y limits (y1, y2) of the plot.

Value

No return value (called for side effects).

Note

By default, the plotted intervals are unadjusted (i.e., pointwise) intervals. For simultaneous intervals, use adjust = "Bonferroni" or adjust = "Scheffe". For the Bonferroni adjustment, you must specify a value for k, the number of intervals for which the coverage is to hold simultaneously. For the Scheffe adjustment, specifying a value for k is only required when interval = "prediction"; if interval = "confidence", k is set equal to \(p\), the number of regression parameters. For example, if object is a simple linear regression model, then calling plotFit with interval = "confidence" and adjust = "Scheffe" will plot the Working-Hotelling band.

Confidence/prediction bands for nonlinear regression (i.e., objects of class stats::nls()) are based on the linear approximation described in Bates & Watts (2007).

References

Bates, D. M., and Watts, D. G. (2007) Nonlinear Regression Analysis and its Applications. Wiley.

Florent Baty, Christian Ritz, Sandrine Charles, Martin Brutsche, Jean-Pierre Flandrois, Marie-Laure Delignette-Muller (2015). A Toolbox for Nonlinear Regression in R: The Package nlstools. Journal of Statistical Software, 66(5), 1-21.

Examples