pattL.fit {prefmod}

Function to fit a pattern model for ratings (Likert items)

Description

Function to fit a pattern model for ratings/Likert items (transformed to paired comparisons) allowing for missing values using a CL approach.

Usage

pattL.fit(obj, nitems, formel = ~1, elim = ~1, resptype = "rating",
        obj.names = NULL, undec = TRUE, ia = FALSE, NItest = FALSE,
        pr.it = FALSE)

Arguments

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 omitted 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 "rating" by default and is reserved for future usage. Any other specification will not change the behaviour of pattL.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.

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

Details

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).

Value

pattL.fit returns an object of class pattMod. The function print (i.e., print.pattMod) can be used to print the results and the function patt.worth to produce a matrix of worth parameters.

An object of class pattMod is a list containing the following components:

coefficients

estimates

ll

log-likelihood of the model

fl

log-likelihood of the saturated model

call

function call

result

a list of results from the fitting routine (see Value of nlm).

envList

a list with further fit details like subject covariates design structure covdesmat, paired comparison response pattern matrix Y, etc.

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.

Input Data

The responses have to be coded as consecutive integers starting with 1 (or 0). The value of 1 (0) means highest ‘endorsement’ (agreement) according to the underlying scale. Missing values are coded as NA, rows with less than 2 valid responses 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 see issp2000.

Warning

The size of the table to be analysed increases dramatically with the number of items. For ratings (Likert items) the number of paired comparison response categories is always three. The number of rows of the table to set up the design matrix is initially (2 * number of categories - 1) ^ (number of items), e.g., for six items with 5 response categories each this is 531441. A reasonable maximum number of items with five response categories to be analysed with pattern models is 7.

Author(s)

Reinhold Hatzinger

See Also

patt.design, pattPC.fit, pattR.fit

Examples

# fit only four items
music4 <- music[, c("jazz", "blue", "folk", "rap")]
pattL.fit(music4, nitems = 4)

# fit additional undecided effect
pattL.fit(music4, nitems = 4, undec = TRUE)

# fit dependence parameters
## Not run: pattL.fit(music4, nitems = 4, undec = TRUE, ia = TRUE)

# check for ignorable missing
pattL.fit(music4, nitems = 4, undec = TRUE, NItest = TRUE)