Analysis of a two-way table with one observation per cell

Fits an additive model using either row and column means or Tukey's median polish procedure

Usage

twoway(x, ...)

# Default S3 method
twoway(
  x,
  method = c("mean", "median"),
  ...,
  name = deparse(substitute(x)),
  responseName = attr(x, "response"),
  varNames = names(dimnames(x))
)

Arguments

x: a numeric matrix or data frame.
...: other arguments passed down
method: one of "mean" or "median"
name: name for the input dataset
responseName: name for the response variable
varNames: names for the Row and Column variables

Value

An object of class c("twoway") with the following named components:

overall: the fitted constant term.
roweff: the fitted row effects.
coleff: the fitted column effects.
residuals: the residuals.
name: the name of the dataset.
rownames: the names for the rows
colnames: the names for the columns
method: the fitting method
varNames: the names of the row and column variables
responseName: the name of the response variable
compValue: the comparison values, for the diagnostic plot
slope: the slope value, for the diagnostic plot
power: the suggested power transformation, 1-slope

An object of class "twoway", but supplemented by additional components used for labeling

Details

The rownames(x) are used as the levels of the row factor and the colnames(x) are the levels of the column factor. For a numeric matrix, the function uses the names(dimnames(x)) as the names of these variables, and, if present, a responseName attribute as the name for the response variable.

References

Tukey, J. W. (1977). Exploratory Data Analysis, Reading MA: Addison-Wesley.

Friendly, M. (1991). SAS System for Statistical Graphics Cary, NC: SAS Institute

Author

Michael Friendly

Examples

data(taskRT)
twoway(taskRT)
#> 
#> Mean decomposition (Dataset: "taskRT"; Response: RT)
#> Residuals bordered by row effects, column effects, and overall
#> 
#>         Topic
#> Task       topic1    topic2    topic3    topic4      roweff   
#>          + --------- --------- --------- --------- + ---------
#>   Easy   | -0.055833  0.090833  0.004167 -0.039167 : -0.864167
#>   Medium |  0.119167  0.075833 -0.410833  0.215833 : -0.059167
#>   Hard   | -0.063333 -0.166667  0.406667 -0.176667 :  0.923333
#>          + ......... ......... ......... ......... + .........
#>   coleff | -0.831667 -0.288333  0.358333  0.761667 :  4.181667
#>