prefmod (version 0.8-37)

llbt.fit: Function to fit an LLBT

Description

Function to fit an LLBT using an ELIMINATE feature

Usage

llbt.fit(y, Xmodel, q, ncat, maxiter = 100)

Arguments

y

response, usually counts

Xmodel

design matrix

q

number of parameters to eliminate (usually number of comparisons times number of subject covariate levels

ncat

number of response categories

maxiter

maximum number of iterations (default 100)

Author

Reinhold Hatzinger

Details

Be careful when specifying the design matrix. Since there is no extrinsic aliasing the matrix must have full rank. Usually, one of the design columns for object must be left out.

References

Hatzinger, R., & Francis, B. (2004). Fitting paired comparison models in R. tools:::Rd_expr_doi("10.57938/d24575d2-77d2-4a7e-a63d-c787cc9d1906")

Examples

# fit basic model casewise
mfr <- llbt.design(cemspc, nitems = 6,
    objnames = c("lo", "pa", "mi", "sg", "ba", "st"),
    casewise = TRUE)
mm   <- model.matrix(~ lo+pa+mi+sg+ba + g1, data = mfr)
X    <- mm[, -1]
p    <- ncol(X)
ncat <- 3
q    <- length(levels(mfr$mu)) * length(levels(mfr$CASE))
llbt.fit(mfr$y, X, q, ncat)


# fit the (aggregated) model with one subject covariate
mfr <- llbt.design(cemspc, nitems = 6,
    objnames = c("lo", "pa", "mi", "sg", "ba", "st"),
    cov.sel = "ENG")
eng  <- mfr$ENG
eng  <- factor(eng)
mm   <- model.matrix(~ lo+pa+mi+sg+ba + g1 + (lo+pa+mi+sg+ba):eng, data = mfr)
X    <- mm[, -1]
q    <- length(levels(mfr$mu)) * length(levels(eng))
ncat <- 3
llbt.fit(mfr$y, X, q, ncat)