latent_cor: Latent correlations

In dexter: Data Management and Analysis of Tests

Latent correlations

Description

Estimates correlations between latent traits using plausible values as described in Marsman, et al. (2022). An item_property is used to distinguish the different scales.

Usage

latent_cor(
  dataSrc,
  item_property,
  predicate = NULL,
  nDraws = 500,
  hpd = 0.95,
  use = "complete.obs"
)

Arguments

dataSrc	A connection to a dexter database or a data.frame with columns: person_id, item_id, item_score and the item_property
item_property	The name of the item property used to define the domains. If dataSrc is a dexter db then the item_property must match a known item property. If datasrc is a data.frame, item_property must be equal to one of its column names.
predicate	An optional expression to subset data, if NULL all data is used
nDraws	Number of draws for plausible values
hpd	width of Bayesian highest posterior density interval around the correlations, value must be between 0 and 1.
use	Only complete.obs at this time. Respondents who don't have a score for one or more scales are removed.

Details

This function uses plausible values so results may differ slightly between calls.

Value

List containing a estimated correlation matrix, the corresponding standard deviations, and the lower and upper limits of the highest posterior density interval

References

Marsman, M., Bechger, T. M., & Maris, G. K. (2022). Composition algorithms for conditional distributions. In Essays on Contemporary Psychometrics (pp. 219-250). Cham: Springer International Publishing.

dexter documentation built on Sept. 11, 2024, 6:42 p.m.