plausible_scores: Draw plausible test scores
In dexter: Data Management and Analysis of Tests

plausible_scores

R Documentation

Draw plausible test scores

Description

Draw plausible, i.e. posterior predictive sumscores on a set of items.

Usage

plausible_scores(
  dataSrc,
  parms = NULL,
  predicate = NULL,
  items = NULL,
  parms_draw = c("sample", "average"),
  covariates = NULL,
  nPS = 1,
  prior_dist = c("normal", "mixture"),
  keep.observed = TRUE,
  by_item = FALSE,
  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.
`parms_draw`	when the item parameters are estimated Bayesianly (see: `fit_enorm`), parms_draw specifies whether to use a sample(a different item parameter draw for each plausible values draw) or the posterior mean of the item draws. Alternatively, it can be an integer specifying a specific draw. Ignored when parms is not estimated Bayesianly.
`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 that indicates nominal categories, e.g. gender or school If dataSrc is a data.frame, it must contain the covariate.
`nPS`	Number of plausible testscores to generate per person.
`prior_dist`	use a normal prior for the plausible values or a mixture of two normals. A mixture is only possible when there are no covariates.
`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.
`by_item`	return scores per item instead of sumscores
`merge_within_persons`	If a person took multiple booklets, this indicates whether plausible scores are generated per person (TRUE) or per booklet (FALSE)