Chart an lm or glm model or diagnose its residuals visually

The methods autoplot() or chart() for lm or glm objects. If type = model (by default for chart()), a scatterplot with the model superimposed is produced, providing the model has only two numeric variables (or a combination of these). The other types allow to analyze the residuals of the model.

# S3 method for class 'lm'
chart(
  data,
  type = "model",
  ...,
  origdata = NULL,
  title,
  labels = "AUTO",
  name = deparse(substitute(data)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

autoplot.lm(
  object,
  origdata = NULL,
  type = c("model", "resfitted", "qqplot", "scalelocation", "cooksd", "resleverage",
    "cookleverage", "reshist", "resautocor"),
  title,
  xlab,
  ylab,
  ...,
  name = deparse(substitute(object)),
  lang = getOption("SciViews_lang", "en"),
  env = parent.frame()
)

Arguments

data: A lm or glm model.
type: The type of plot: "model", "resfitted", "qqplot", "scalelocation", "cooksd", "resleverage", "cookleverage", "reshist" or "resautocor". For chart(), can also be provided as chart$type(....). chart() also uses "residuals" that constructs a combined figure with resfitted, qqplot, scalelocation and resleverage.
...: Additional arguments passed to the chart.“
origdata: The original dataset this model was fitted to. Only required for type = model and in case untransformed X variable is not in the model.
title: A title for the plot. If not provided, a default title is computed.
labels: A vector of four character strings, one for each plot done with chart$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

library(chart)
data(trees, package = "datasets")
trees_lm <- lm(Volume ~ Girth, data = trees)
chart(trees_lm) # origdata not needed because untransformed variables
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the modelit package.
#>   Please report the issue at <https://github.com/SciViews/modelit/issues>.

# Residuals analysis
chart$resfitted(trees_lm)

chart$qqplot(trees_lm)

chart$scalelocation(trees_lm)

chart$cooksd(trees_lm)

chart$resleverage(trees_lm)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the modelit package.
#>   Please report the issue at <https://github.com/SciViews/modelit/issues>.

chart$cookleverage(trees_lm)

chart$reshist(trees_lm, bins = 15)

chart$resautocor(trees_lm)


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


trees_lm2 <-  lm(Volume ~ log(Girth), data = trees)
chart(trees_lm2, origdata = trees) # origdata needed, cf. transformed Girth

trees_lm3 <- lm(Volume ~ Girth + Height, data = trees)
# chart(trees_lm3) # Error because more than 2 variables!
# Polynomial regressions work too
trees_lm4 <- lm(Volume ~ I(Girth^2) + Girth, data = trees)
chart(trees_lm4)

# or using poly()
trees_lm5 <- lm(Volume ~ poly(Girth, 3), data = trees)
chart(trees_lm5, origdata = trees) # origdata required here!