CORRECTION article

Front. Appl. Math. Stat., 16 May 2017

Sec. Quantitative Psychology and Measurement

Volume 3 - 2017 | https://doi.org/10.3389/fams.2017.00008

Corrigendum: A Review of R-packages for Random-Intercept Probit Regression in Small Clusters

  • Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium

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:

SEM can 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.

Statements

Conflict of interest

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.

    RosseelY. lavaan: An R package for structural equation modeling. J Stat Softw. (2012) 48:136. 10.18637/jss.v048.i02

Summary

Keywords

categorical data analysis, multilevel modeling, mixed models, structural equation modeling, monte carlo studies

Citation

Josephy H, Loeys T and Rosseel Y (2017) Corrigendum: A Review of R-packages for Random-Intercept Probit Regression in Small Clusters. Front. Appl. Math. Stat. 3:8. doi: 10.3389/fams.2017.00008

Received

25 April 2017

Accepted

04 May 2017

Published

16 May 2017

Volume

3 - 2017

Edited and reviewed by

Mike W.-L. Cheung, National University of Singapore, Singapore

Updates

Copyright

*Correspondence: Haeike Josephy

This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Applied Mathematics and Statistics

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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