- Published: December 11, 2021
- Updated: December 11, 2021
- University / College: Griffith University
- Language: English
- Downloads: 40
A corrigendum on
A Review of R-packages for Random-Intercept Probit Regression in Small Clusters
by Josephy, H., Loeys, T., and Rosseel, Y. (2016). Front. Appl. Math. Stat. 2, 1–13. doi: 10. 3389/fams. 2016. 00018
In the original research aricle, there was an error. In the appendix (section 8 in the originally submitted article), we forgot to include part of the R -code in which a data set is defined for the sem -function from the package lavaan [ 1 ]. Furthermore, we would like to change the ordering of some of the R -code. The appendix is uploaded as “ Presentation 1. pdf” on the Frontiers website, and not as “ Section 8” in the original article.
A correction has been made to the Appendix Section 8. 4, SEM methods, first paragraph:
SEMcan be applied to the data by use of the function sem from the package lavaan [ 1 ]. This R -function allows both the theta- and delta-parametrization (see Section 3. 2) but since these are practically equivalent, we only focussed on the latter. As the delta-parameterization and the DWLS estimator with robust standard errors are executed by default, we do not need to specify any additional options for this function. Note that the data is now in wide format, with the following model -specification for a within-cluster predictor in clusters of size two:
Data <- data. frame(y0 = y0, y1 = y1, x1 = x1, x0 = x0)
model <- ‘ int= 1*y0+1*y1
y0~a1*x0; y0| a0*t1; y0 ~~ v1*y0
y1~a1*x1; y1| a0*t1; y1 ~~ v1*y1 ’
fit <- sem(model, ordered= c(" y0"," y1"), data= Data)
summary(fit)
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1. Rosseel Y. lavaan: An R package for structural equation modeling. J Stat Softw.(2012)48: 1–36. doi: 10. 18637/jss. v048. i02
