Generate plausible i.e., posterior predictive sumscores on a set of items. A typical use of this function is to generate plausible scores on a complete item bank when data is collected using an incomplete design

plausible_scores(
dataSrc,
parms = NULL,
predicate = NULL,
items = NULL,
covariates = NULL,
keep.observed = TRUE,
nPS = 1,
merge_within_persons = FALSE
)

## Arguments

dataSrc

a connection to a dexter database, a matrix, or a data.frame with columns: person_id, item_id, item_score

parms

An object returned by function fit_enorm and containing parameter estimates. If parms is given the function provides plausible scores conditional on the item parameters. These are considered known. If parms is NULL, Bayesian parameters are calculated from the datasrc

predicate

an expression to filter data. If missing, the function will use all data in dataSrc

items

vector of item_id's, this specifies the itemset to generate the testscores for. If items is NULL all items occurring in dataSrc are used.

covariates

name or a vector of names of the variables to group the population, used to update the prior. A covariate must be a discrete person covariate (e.g. not a float) that indicates nominal categories, e.g. gender or school If dataSrc is a data.frame, it must contain the covariate.

keep.observed

If responses to one or more of the items have been observed, the user can choose to keep these observations or generate new ones.

nPS

Number of plausible testscores to generate per person.

merge_within_persons

If a person took multiple booklets, this indicates whether plausible scores are generated per person (TRUE) or per booklet (FALSE)

## Value

A data.frame with columns booklet_id, person_id, booklet_score and nPS plausible scores named PS1...PSn.