nmca is conjoint measurement: it takes a rank order over profiles
and the categorical factors describing them, and estimates the part-worth
of each factor level. It draws a Hasse diagram of the order as part of the
output.
Arguments
- formula
rank ~ f1 + f2, where the response is the rank order and the right-hand side names the factors.- data
a data frame. Factor levels are numbered from 1 upwards, and a value outside that range is rejected by TDA with the record it occurred in.
- options
a named list of further TDA options, passed through.
- dir
working directory.
- ...
passed to
tda_run.
See also
Other clustering:
TDA_CLUSTER,
TDA_MDS,
tda_cutree(),
tda_dma(),
tda_pdatd()
Examples
d <- expand.grid(price = 1:2, brand = 1:2)
d$rank <- c(4, 2, 3, 1)
cj <- tda_conjoint(rank ~ price + brand, d)
cat(tda_payload(cj$run), sep = "\n") # the nmca section itself
#> nmca(...)=...
#> Non-metric conjoint analysis. Current memory: 390161 bytes.
#> Rank order variable: Vrank
#> Dimension Variable Categories
#> 1 Vprice 2 : 1 2
#> 2 Vbrand 2 : 1 2
#> Found: ( 2 , 1 ) ( 2 , 1 )
#> Found 1 partial orders.