llbtPC.fit {prefmod} | R Documentation |
Function to fit a loglinear Bradley-Terry for paired comparisons allowing subject covariates and undecided response categories.
llbtPC.fit(obj, nitems, formel = ~1, elim = ~1, resptype = "paircomp", obj.names = NULL, undec = FALSE)
obj |
either a dataframe or the path/name of the datafile to be read. |
nitems |
the number of compared objects, not the number of comparisons |
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 "paircomp" by default and is reserved for future usage.
Any other specification will not change the behaviour of pattPC.fit
|
obj.names |
character vector with names for objects. |
undec |
for paired comparisons with a undecided/neutral category,
a common parameter will be estimated if undec = 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 specification 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).
llbtPC.fit
returns an object of class llbtMod
. This object
is basically a gnm
object with an additional element envList
.
This is a list with further details like the subject covariates
design structure covdesmat
, the model specification (formel
and elim
), the object names (obj.names
), the number of
items (nobj
) and comparisons (ncomp
), etc.
The function llbt.worth
can be used to
produce a matrix of estimated worth parameters.
The responses have to be coded as 0/1 for paired comparisons without undecided category (0 means first object in a comparison preferred) or 0/1/2 for paired comparisons with an undecided category (where 1 is the undecided category). 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 responses to the paired comparisons (where the mandatory order of comparisons is (12) (13) (23) (14) (24) (34) (15) (25) etc.), 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 see cemspc
or the file cemspc.dat
in
the package's data/
directory.
The function llbtPC.fit
is a wrapper function for gnm
and
was designed to facilitate fitting of LLBTs with subject covariates and
undecided categories. More specialised setups (e.g., object-specific covariates)
can be obtained using
llbt.design
and then calling gnm
(or glm
)
directly (see Examples for llbt.design
).
Reinhold Hatzinger
## cems universities example data(cemspc) res0<-llbtPC.fit(cemspc, nitems=6, formel=~1, elim=~ENG, undec=TRUE) res1<-llbtPC.fit(cemspc, nitems=6, formel=~ENG, elim=~ENG, undec=TRUE) anova(res1, res0) llbt.worth(res1)