plotFit: Plotting fitted models

Description

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

Usage

plotFit(object, ...)
# S3 method for default
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 lm, glm, or 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 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.

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.

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).

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

Run this code

# NOT RUN {
# A nonlinear least squares example (see ?datasets::Puromycin and 
# ?investr::predFit)
data(Puromycin, package = "datasets")
Puromycin2 <- Puromycin[Puromycin$state == "treated", ][, 1:2]
Puro.nls <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin2,
                start = c(Vm = 200, K = 0.05))
plotFit(Puro.nls, interval = "both", pch = 19, shade = TRUE, 
        col.conf = "skyblue4", col.pred = "lightskyblue2")  
# }