vcd (version 1.1-1)

plot.loglm: Visualize Fitted Log-linear Models

Description

Visualize fitted "loglm" objects by mosaic or association plots.

Usage

## S3 method for class 'loglm':
plot(x, panel = mosaic, type = c("observed", "expected"),
  residuals_type = c("pearson", "deviance"), gp = shading_hcl, gp_args = list(),
  ...)

Arguments

x

a fitted "loglm" object, see loglm.

panel

a panel function for visualizing the observed values, residuals and expected values. Currently, mosaic and assoc in vcd.

type

a character string indicating whether the observed or the expected values of the table should be visualized.

residuals_type

a character string indicating the type of residuals to be computed.

gp

object of class "gpar", shading function or a corresponding generating function (see details and shadings). Ignored if shade = FALSE.

gp_args

list of arguments for the shading-generating function, if specified.

...

Other arguments passed to the panel function.

Value

• The "structable" visualized is returned invisibly.

Details

The plot method for "loglm" objects by default visualizes the model using a mosaic plot (can be changed to an association plot by setting panel = assoc) with a shading based on the residuals of this model. The legend also reports the corresponding p value of the associated goodness-of-fit test. The mosaic and assoc methods are simple convenience interfaces to this plot method, setting the panel argument accordingly.

See Also

loglm, assoc, mosaic, strucplot

Examples

## mosaic display for PreSex model
data("PreSex")
fm <- loglm(~ PremaritalSex * ExtramaritalSex * (Gender + MaritalStatus),
  data = aperm(PreSex, c(3, 2, 4, 1)))
fm
## visualize Pearson statistic
plot(fm, split_vertical = TRUE)
## visualize LR statistic
plot(fm, split_vertical = TRUE, residuals_type = "deviance")

## conditional independence in UCB admissions data
data("UCBAdmissions")
fm <- loglm(~ Dept * (Gender + Admit), data = aperm(UCBAdmissions))

## use mosaic display
plot(fm, labeling_args = list(abbreviate = c(Admit = 3)))

## and association plot
plot(fm, panel = assoc)
assoc(fm)