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Extract and format item parameter estimates and their covariance matrix

get_model_parms.Rd

Takes a 1-factor model fit or list of 1-factor model fits from mirt or cfa and formats the item parameter estimates and their covariance matrix for use in other robustDIF functions.

Usage

get_model_parms(object, cluster = NULL)

Arguments

object

model fit from a multigroup analysis or list of model fits for each group for a 1-factor model. See Details.

cluster

optional clustering IDs used to compute a cluster-robust covariance matrix for mirt fits. For MultipleGroupClass, provide an atomic vector of length equal to the number of observations. For a list of SingleGroupClass fits, provide a list of cluster-ID vectors (one per model).

Value

A three-element list:

  • par.names: list with internal and original parameter names.

  • est: list (one element per group) of data frames containing item parameters by row (a1, d1, d2, ...).

  • var.cov: list (one element per group) of covariance matrices for the corresponding parameter vectors.

Details

The function takes a fitted 1-factor multigroup model or list of fitted 1-factor single group models. The factor must be standardized (i.e., variance = 1) and the covariance matrix be asymptotically correct. Currently, the function accepts:

  • a mirt object of class SingleGroupClass or MultipleGroupClass with SE = TRUE (to return covariance matrix) and itemtype of any combination of "2PL", "graded", or "gpcm".

  • a lavaan object estimated from cfa with std.lv = TRUE.

When cluster is supplied for mirt fits, the covariance matrix is computed using a cluster-robust sandwich estimator with Oakes bread and cluster-summed empirical scores. A CR1 finite-sample correction is applied: (G/(G-1)) * ((N-1)/(N-p)), where G is the number of clusters, N is the number of observations, and p is the number of free parameters. It is possible to use fits from other software with robustDIF functions, but the parameter estimates and their covariance matrices must be formatted identically to what is returned by get_model_parms. For details, see the documentation for the example dataset rdif.eg.

See also

rdif.eg