GGally (version 1.3.2)

ggnostic: ggnostic - Plot matrix of statistical model diagnostics

Description

ggnostic - Plot matrix of statistical model diagnostics

Usage

ggnostic(model, ..., columnsX = attr(data, "var_x"), columnsY = c(".resid",
  ".sigma", ".hat", ".cooksd"), columnLabelsX = attr(data, "var_x_label"),
  columnLabelsY = gsub("\\.", " ", gsub("^\\.", "", columnsY)),
  xlab = "explanatory variables", ylab = "diagnostics",
  title = paste(deparse(model$call, width.cutoff = 500L), collapse = "\n"),
  continuous = list(default = ggally_points, .fitted = ggally_points, .se.fit
  = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat =
  ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd =
  ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid),
  combo = list(default = ggally_box_no_facet, fitted = ggally_box_no_facet,
  .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat =
  ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd =
  ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid),
  discrete = list(default = ggally_ratio, .fitted = ggally_ratio, .se.fit =
  ggally_ratio, .resid = ggally_ratio, .hat = ggally_ratio, .sigma =
  ggally_ratio, .cooksd = ggally_ratio, .std.resid = ggally_ratio),
  data = broomify(model))

Arguments

model

statistical model object such as output from stats::lm or stats::glm

...

arguments passed directly to ggduo

columnsX

columns to be displayed in the plot matrix. Defaults to the predictor columns of the model

columnsY

rows to be displayed in the plot matrix. Defaults to residuals, leave one out sigma value, diagonal of the hat matrix, and Cook's Distance. The possible values are the response variables in the model and the added columns provided by broom::augment(model). See details for more information.

columnLabelsX, columnLabelsY

column and row labels to display in the plot matrix

xlab, ylab, title

plot matrix labels passed directly to ggmatrix

continuous, combo, discrete

list of functions for each y variable. See details for more information.

data

data defaults to a 'broomify'ed model object. This object will contain information about the X variables, Y variables, and multiple broom outputs. See broomify(model) for more information

`columnsY`

broom::augment() collects data from the supplied model and returns a data.frame with the following columns (taken directly from broom documentation). These columns are the only allowed values in the columnsY parameter to ggnostic.

.resid

Residuals

.hat

Diagonal of the hat matrix

.sigma

Estimate of residual standard deviation when corresponding observation is dropped from model

.cooksd

Cooks distance, cooks.distance

.fitted

Fitted values of model

.se.fit

Standard errors of fitted values

.std.resid

Standardized residuals

response variable name

The response variable in the model may be added. Such as "mpg" in the model lm(mpg ~ ., data = mtcars)

`continuous`, `combo`, `discrete` types

Similar to ggduo and ggpairs, functions may be supplied to display the different column types. However, since the Y rows are fixed, each row has it's own corresponding function in each of the plot types: continuous, combo, and discrete. Each plot type list can have keys that correspond to the broom::augment() output: ".fitted", ".resid", ".std.resid", ".sigma", ".se.fit", ".hat", ".cooksd". An extra key, "default", is used to plot the response variables of the model if they are included. Having a function for each diagnostic allows for very fine control over the diagnostics plot matrix. The functions for each type list are wrapped into a switch function that calls the function corresponding to the y variable being plotted. These switch functions are then passed directly to the types parameter in ggduo.

Examples

# NOT RUN {
# small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive
data(mtcars)

# use mtcars dataset and alter the 'am' column to display actual name values
mtc <- mtcars
mtc$am <- c("0" = "automatic", "1" = "manual")[as.character(mtc$am)]

# step the complete model down to a smaller model
mod <- stats::step(stats::lm(mpg ~ ., data = mtc), trace = FALSE)

# display using defaults
pm <- ggnostic(mod)
p_(pm)

# color by am value
pm <- ggnostic(mod, mapping = ggplot2::aes(color = am))
p_(pm)

# turn resid smooth error ribbon off
pm <- ggnostic(mod, continuous = list(.resid = wrap("nostic_resid", se = FALSE)))
p_(pm)


## plot residuals vs fitted in a ggpairs plot matrix
dt <- broomify(mod)
pm <- ggpairs(
  dt, c(".fitted", ".resid"),
  columnLabels = c("fitted", "residuals"),
  lower = list(continuous = ggally_nostic_resid)
)
p_(pm)
# }