Chart an nls model or diagnose its residuals visually

The methods autoplot() or chart() for nls objects. If type = model (by default for chart()), a scatterplot with the model superimposed is produced,. The other types allow to analyze the residuals of the model.

Usage

# S3 method for nls
chart(
  data,
  type = "model",
  ...,
  title,
  labels = "AUTO",
  name = deparse(substitute(data)),
  lang = getOption("data.io_lang", "en"),
  env = parent.frame()
)

autoplot.nls(
  object,
  type = c("model", "resfitted", "qqplot", "scalelocation", "reshist", "resautocor"),
  title,
  xlab,
  ylab,
  ...,
  name = deparse(substitute(object)),
  lang = getOption("data.io_lang", "en"),
  env = parent.frame()
)

Arguments

data: A nls model.
type: The type of plot: "model", "resfitted", "qqplot", "scalelocation", "reshist" or "resautocor". For chart(), can also be provided as chart$type(....). chart() also uses "residuals" that constructs a combined figure with resfitted, qqplot, scalelocation and resautocor.
...: Additional arguments passed to the chart.
title: A title for the plot. If not provided, a default title is computed.
labels: A vector of four character strings, one for each plot done with chart$residuals().
name: The name of the model. If not provided, it is the name of the model object by default.
lang: The language to use for titles and labels, currently only "en" or "fr".
env: The environment to evaluate code. It is parent.frame() by default, and there is no reasons to change it, unless you really know what you are doing!
object: Idem
xlab: A label for the X axis. A default label is proposed if it is not provided.
ylab: A label for the Y axis (with default if not provided).

Value

The ggplot object produced.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(weight ~ SSlogis(Time, Asym, xmid, scal), data = chick1)
library(chart)
chart(chick1_logis)

# Residuals analysis
chart$resfitted(chick1_logis)

chart$qqplot(chick1_logis)

chart$scalelocation(chick1_logis)

chart$reshist(chick1_logis, bins = 15)

chart$resautocor(chick1_logis)


# The four most important residual analysis plots in one figure
chart$residuals(chick1_logis)