plotFit: Plotting fitted models

In investr: Inverse Estimation/Calibration Functions

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, ...)

## 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 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.
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 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.

See Also

plotfit

Examples

# 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")  

investr documentation built on March 31, 2022, 9:06 a.m.