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.
latent_cor(
dataSrc,
item_property,
predicate = NULL,
nDraws = 500,
hpd = 0.95,
use = "complete.obs"
)A connection to a dexter database or a data.frame with columns: person_id, item_id, item_score and the 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.
An optional expression to subset data, if NULL all data is used
Number of draws for plausible values
width of Bayesian highest posterior density interval around the correlations, value must be between 0 and 1.
Only complete.obs at this time. Respondents who don't have a score for one or more scales are removed.
List containing a estimated correlation matrix, the corresponding standard deviations, and the lower and upper limits of the highest posterior density interval and the complete mcmc sample
This function uses plausible values so results may differ slightly between calls.
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.