The data, taken from Ries and Smith (1963)
, is a dataset examining
the distribution of a large sample of customers, classified over
four factors:
Softness of water used (3 levels: soft, medium or hard),
Expressed preference for brand M or X after blind test (2 levels:
Brand M or Brand X), Previously used brand M (2 levels: yes
or no), and Temperature of landry water (2 levels: hot or
cold). It is therefore a 3 × 2 × 2 × 2 design with 24 cells.
DetergentAn object of class list.
Ries P, Smith H (1963). “The use of chi-square for preference testing in multidimensional problems.” Chemical Engineering Progress, 59, 39-43.
# convert the data to a data.frame
dta <- data.frame(Detergent)
# run the anofa analysis
if (FALSE) {
w <- anofa( Freq ~ Temperature * M_User * Preference * Water_softness, dta)
# make a plot with all the factors
anofaPlot(w)
# ... or with just a few factors
anofaPlot(w, ~ Preference * M_User )
anofaPlot(w, ~ Temperature )
# extract simple effects
e <- emFrequencies(w, ~ M_User | Preference )
}