pattR.fit {prefmod} | R Documentation |
Function to fit a pattern model for (partial) rankings (transformed to paired comparisons) allowing for missing values using a CL approach.
pattR.fit(obj, nitems, formel = ~1, elim = ~1, resptype = "ranking", ia = FALSE, NItest = FALSE, pr.it = FALSE)
obj |
either a dataframe or the path/name of the datafile to be read. |
nitems |
the number of items |
formel |
the formula for subject covariates to fit different preference scales for the objects (see below). |
elim |
the formula for the subject covariates that specify the table
to be analysed. If ommitted and formel is not ~1 then
elim will be set to the highest interaction between all terms
contained in formel . If elim is specified, the terms
must be separated by the * operator. |
resptype |
is "ranking" by default and is reserved for future usage.
Any other specification will not change the behaviour of pattR.fit
|
ia |
interaction parameters between comparisons that have one object
in common if ia = TRUE .
|
NItest |
separate estimation of object parameters for complete and
incomplete patterns if NItest = TRUE . Currently,
NItest is set to FALSE if subject covariates are specified.
|
pr.it |
a dot is printed at each iteration cycle if set to TRUE
|
Models including categorical subject covariates can be fitted using the
formel
and elim
arguments. formel
specifies the
actual model to be fitted. For instance, if specified as
formel=~SEX
different preference scale for the objects will be
estimated for males and females. For two or more covariates,
the operators +
or *
can be used to model main or interaction
effects, respectively. The operator :
is not allowed. See also
formula
.
The spcification for elim
follows the same rules as for
formel
. However, elim
specifies the basic contingency
table to be set up but does not specify any covariates to be fitted.
This is done using formel
.
If, e.g., elim=SEX
but formel=~1
,
then the table is set up as if SEX
would be fitted but only one global
preference scale is computed. This feature
allows for the succesive fitting of nested models to enable the use of
deviance differences for model selection (see example below).
On return the function provides a list with elements
result |
a list of results from the fitting routine (see Value of
nlm .
|
partsList |
a list of the basic data structures for each subgroup
defined by crossing all covariate levels and different missing value
patterns. Each element of partsList is again a list containing
counts, missing value pattern, the CL matrix represented as a vector, and
the specification of the covariates. Use str to inspect
the elements and see example below.
|
The responses have to be coded as consecutive integers starting with 1.
The value of 1 means highest rank according to the underlying scale.
Each column in the data file corresponds to one of the ranked objects. For example,
if we have 3 objects denoted by A
,B
,and C
, with
corresponding columns in the data matrix, the response pattern (3,1,2)
represents: object B
ranked highest, C
ranked second, and
A
ranked lowest. Missing values are coded as NA
,
ties are not allowed (in that case use pattL.fit
.
Rows with less than 2 ranked objects are removed from the fit
and a message is printed.
Optional subject covariates have to be specified such that the categories are represented by consecutive integers starting with 1. Rows with missing values for subject covariates are removed from the data and a message is printed. The leftmost columns in the data must be the rankings, optionally followed by columns for categorical subject covariates.
The data specified via obj
are supplied using either a data frame
or a datafile in which case obj
is a path/filename. The input
data file if specified must be a plain text file with variable names in
the first row as readable via the command read.table(datafilename,
header = TRUE)
.
For an example without covariates and no mising values
see salad
or the file salad.dat
in
the package's data/
directory.
The size of the table to be analysed increases dramatically
with the number of items . For rankings the number of
paired comparison response categories is always two. The number of
rows of the table used to set up the design matrix is factorial(number of items)
.
For instance, for nine objects this is 362880.
A reasonale maximum number of items is 8.
The option NItest = TRUE
has to be used with care. The meaning of missing
responses is not obvious with partial rankings. Are the corresponding values
really missing or just not chosen.
Reinhold Hatzinger
patt.design
, pattL.fit
, pattPC.fit
## fit of Critchlov & Fligner (1991) Salad Dressings Data data(salad) pattR.fit(salad, nitems=4) # alternatively use glm() with patt.design() sal<-patt.design(salad,nitems=4,resptype="ranking") glm(y~A+B+C+D,family=poisson,data=sal)