| Title: | SVD-Based Estimation for Exploratory Item Factor Analysis |
| Version: | 1.0.1 |
| Author: | Haoran Zhang [aut, cre], Yunxiao Chen [aut], Xiaoou Li [aut] |
| Maintainer: | Haoran Zhang <hrzhang16@gmail.com> |
| Description: | Provides singular value decomposition based estimation algorithms for exploratory item factor analysis (IFA) based on multidimensional item response theory models. For more information, please refer to: Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by singular value decomposition. Psychometrika, 1-15, <doi:10.1007/s11336-020-09704-7>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 3.1) |
| Imports: | GPArotation, mirtjml, graphics, stats |
| NeedsCompilation: | no |
| Packaged: | 2025-04-21 02:35:17 UTC; zhanghaoran |
| Repository: | CRAN |
| Date/Publication: | 2025-04-21 03:00:02 UTC |
Item Factor Analysis by Singular Value Decomposition
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 |
K |
the number of factors. |
link |
the link function. 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 |
... |
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, doi:10.1007/s11336-020-09704-7.
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)
Scree plot for singular values.
Description
Scree plot for singular values.
Usage
screeplot_svd(data, link = "logit", epsilon = 1e-04, K_max = 10)
Arguments
data |
the data matrix. Entries are either binary or categorical.
Missing entries should be |
link |
the link function. Possible choices are "logit" and "probit". |
epsilon |
the truncation parameter. Default value is 1e-4. |
K_max |
The maximum number of factors contained in data. Default value is 10. |
References
Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by singular value decomposition. Psychometrika, 1-15, doi:10.1007/s11336-020-09704-7.
Examples
require(mirtjml)
# load a simulated dataset
attach(data_sim)
data <- data_sim$response
screeplot_svd(data, K_max = 10)