mirtsvd: Item Factor Analysis by Singular Value Decomposition

In hrzhang16/mirtsvd: SVD-based Estimation for Exploratory Item Factor Analysis

Description

Item Factor Analysis by Singular Value Decomposition

Usage

mirtsvd(data, K, link = "logit", epsilon = 1e-04, rotation_fn = NULL, ...)

Arguments

data	the data matrix. Entries are either binary or categorical. Missing entries should be NA.
K	the number of factors.
link	the link fucntion. Possible choices are "logit" and "probit".
epsilon	the truncation parameter. Default value is 1e-4.
rotation_fn	rotation applied to the estimated loading matrix. See rotations. If NULL, no rotation would be applied.
...	optional arguments passed to rotation_fn.

Value

The function returns a list with the following components:

loadings

The estimated loading matrix.

rotation

The rotation method.

type

The data type.

number

The number of categories in data.

References

Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by singular value decomposition. Psychometrika, 1-15.

Examples

require(mirtjml)
require(GPArotation)

# load a simulated dataset
attach(data_sim)

data <- data_sim$response
K <- data_sim$K
res <- mirtsvd(data, K, rotation_fn = Varimax)

hrzhang16/mirtsvd documentation built on Aug. 27, 2020, 1:46 a.m.