# sumScoreEAP: Compute the sum-score EAP table In rpf: Response Probability Functions

## Description

Observed tables cannot be computed when data is missing. Therefore, you can optionally omit items with the greatest number of responses missing when conducting the distribution test.

## Usage

 `1` ```sumScoreEAP(grp, ..., qwidth = 6, qpoints = 49L, .twotier = TRUE) ```

## Arguments

 `grp` a list with spec, param, mean, and cov `...` Not used. Forces remaining arguments to be specified by name. `qwidth` positive width of quadrature in Z units `qpoints` number of quadrature points `.twotier` whether to enable the two-tier optimization

## Details

When two-tier covariance structure is detected, EAP scores are only reported for primary factors. It is possible to compute EAP scores for specific factors, but it is not clear why this would be useful because they are conditional on the specific factor sum scores. Moveover, the algorithm to compute them efficiently has not been published yet (as of Jun 2014).

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# see Thissen, Pommerich, Billeaud, & Williams (1995, Table 2) spec <- list() spec[1:3] <- rpf.grm(outcomes=4) param <- matrix(c(1.87, .65, 1.97, 3.14, 2.66, .12, 1.57, 2.69, 1.24, .08, 2.03, 4.3), nrow=4) # fix parameterization param <- apply(param, 2, function(p) c(p[1], p[2:4] * -p[1])) grp <- list(spec=spec, mean=0, cov=matrix(1,1,1), param=param) sumScoreEAP(grp) ```

rpf documentation built on Dec. 8, 2017, 1:05 a.m.