Function to fit a loglinear Bradley-Terry model for paired comparisons
Function to fit a loglinear Bradley-Terry for paired comparisons allowing subject covariates and undecided response categories.
either a dataframe or the path/name of the datafile to be read.
the number of compared objects, not the number of comparisons
the formula for subject covariates to fit different preference scales for the objects (see below).
the formula for the subject covariates that specify the table
to be analysed. If omitted and
character vector with names for objects.
for paired comparisons with a undecided/neutral category,
a common parameter will be estimated if
Models including categorical subject covariates can be fitted using the
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,
* can be used to model main or interaction
effects, respectively. The operator
: is not allowed. See also
The specification for
elim follows the same rules as for
elim specifies the basic contingency
table to be set up but does not specify any covariates to be fitted.
This is done using
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 successive 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
This is a list with further details like the subject covariates
covdesmat, the model specification (
elim), the object names (
obj.names), the number of
nobj) and comparisons (
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
header = TRUE).
For an example see
llbtPC.fit is a wrapper function for
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
directly (see Examples for
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