Visualize Fitted Log-linear Models

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

# 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.

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.

Value

The "structable" visualized is returned invisibly.

Author

Achim Zeileis [email protected]

See also

loglm, assoc, mosaic, strucplot

Examples

library(MASS)
## mosaic display for PreSex model
data("PreSex")
fm <- loglm(~ PremaritalSex * ExtramaritalSex * (Gender + MaritalStatus),
  data = aperm(PreSex, c(3, 2, 4, 1)))
fm
#> Call:
#> loglm(formula = ~PremaritalSex * ExtramaritalSex * (Gender + 
#>     MaritalStatus), data = aperm(PreSex, c(3, 2, 4, 1)))
#> 
#> Statistics:
#>                       X^2 df  P(> X^2)
#> Likelihood Ratio 5.245519  4 0.2630205
#> Pearson          5.238526  4 0.2636870
## 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_labs = c(Admit = 3)))


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

assoc(fm)