gower.agree {goweragreement}

Apply the Bayesian Gower agreement methodology to nominal or ordinal data.

Description

Apply the Bayesian Gower agreement methodology to nominal or ordinal data.

Usage

gower.agree(
  data,
  data.type = c("nominal", "ordinal"),
  dist.type = c("mean", "max"),
  design = c("one-way", "two-way"),
  iter = 10000,
  ...
)

Arguments

data

a matrix of scores. Each row corresponds to a unit, each column to a coder.

data.type

the type of scores to be analyzed, either "nominal" or "ordinal".

dist.type

for ordinal data, whether the row statistics are computed using the mean of the pairwise distances or the maximum pairwise distance.

design

the sampling design, either "one-way" or "two-way". For the former, the units are random and the coders are fixed. For the latter, both the units and the coders are random.

iter

the desired size of the posterior sample. The default value is 10,000.

...

additional arguments for the distance function. These are ignored for nominal data. For ordinal data the range of the scores must be provided via argument range.

Details

This is the package's flagship function. It applies the Bayesian Gower methodology to nominal or ordinal data, and provides an estimate of the posterior mean along with a credible interval.

Value

Function gower.agree returns an object of class "gower", which is a list comprising the following elements.

mu.hat

the estimate of the posterior mean.

mu.sample

the posterior sample.

call

the matched call.

units

the number of units.

coders

the number of coders.

data

the data matrix, sans rows that were removed due to missigness.

data.type

the type of scores, nominal or ordinal.

dist.type

for ordinal data, the manner in which the row statistics were computed.

design

the sampling design, one-way or two-way.

row.stats

the vector of row statistics.

del

the number of rows that were deleted due to missingness.

Examples

# Fit the liver data, using the mean distance for each row of the data matrix.
# The range (which is equal to 4) must be passed to \code{\link{gower.agree}}
# since these data are ordinal and the L1 distance function is used. We assume
# a one-way sampling design for these data, i.e., units are random and coders
# are fixed.

data(liver)
liver = as.matrix(liver)
fit = gower.agree(liver, data.type = "ordinal", range = 4)
summary(fit)