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Correction ARTICLE

Front. Appl. Math. Stat., 16 May 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

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:

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.

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

CrossRef Full Text

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.

Edited and reviewed by: Mike W.-L. Cheung, National University of Singapore, Singapore

Copyright © 2017 Josephy, Loeys and Rosseel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Haeike Josephy, haeike.josephy@ugent.be