imxWlsStandardErrors: Calculate Standard Errors for a WLS Model
In OpenMx: Extended Structural Equation Modelling
View source: R/MxFitFunctionWLS.R
imxWlsStandardErrors R Documentation
Calculate Standard Errors for a WLS Model
Description
This is an internal function used to calculate standard errors for weighted least squares models.
Usage
imxWlsStandardErrors(model)
Arguments
model
An MxModel object with acov (WLS) data
Details
The standard errors for models fit with maximum likelihood are related to the second derivative (Hessian) of the likelihood function with respect to the free parameters. For models fit with weighted least squares a different expression is required. If J is the first derivative (Jacobian) of the mapping from the free parameters to the unique elements of the expected covariance, means, and thresholds, V is the weight matrix used, W is the inverse of the full weight matrix, and U= V J (J' V J)^{-1}, then the asymptotic covariance matrix of the free parameters is
Acov(\theta) = U' W U
with U' indicating the transpose of U.
Value
A named list with components
- SE
The standard errors of the free parameters
- Cov
The full covariance matrix of the free parameters. The square root of the diagonal elements of Cov equals SE.
- Jac
The Jacobian computed to obtain the standard errors.
References
M. W. Browne. (1984). Asymptotically Distribution-Free Methods for the Analysis of Covariance Structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.
F. Yang-Wallentin, K. G. Jöreskog, & H. Luo. (2010). Confirmatory Factor Analysis of Ordinal Variables with Misspecified Models. Structural Equation Modeling, 17, 392-423.
OpenMx documentation built on Oct. 19, 2024, 9:06 a.m.
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View source: R/MxFitFunctionWLS.R
| imxWlsStandardErrors | R Documentation |
Calculate Standard Errors for a WLS Model
Description
This is an internal function used to calculate standard errors for weighted least squares models.
Usage
imxWlsStandardErrors(model)
Arguments
model |
An MxModel object with acov (WLS) data |
Details
The standard errors for models fit with maximum likelihood are related to the second derivative (Hessian) of the likelihood function with respect to the free parameters. For models fit with weighted least squares a different expression is required. If J is the first derivative (Jacobian) of the mapping from the free parameters to the unique elements of the expected covariance, means, and thresholds, V is the weight matrix used, W is the inverse of the full weight matrix, and U= V J (J' V J)^{-1}, then the asymptotic covariance matrix of the free parameters is
Acov(\theta) = U' W U
with U' indicating the transpose of U.
Value
A named list with components
- SE
The standard errors of the free parameters
- Cov
The full covariance matrix of the free parameters. The square root of the diagonal elements of Cov equals SE.
- Jac
The Jacobian computed to obtain the standard errors.
References
M. W. Browne. (1984). Asymptotically Distribution-Free Methods for the Analysis of Covariance Structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.
F. Yang-Wallentin, K. G. Jöreskog, & H. Luo. (2010). Confirmatory Factor Analysis of Ordinal Variables with Misspecified Models. Structural Equation Modeling, 17, 392-423.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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