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

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

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...

Application of random-effects probit regression models.

A random-effects probit model is developed for the case in which the outcome of interest is a series of correlated binary responses. These responses can be obtained as the product of a longitudinal response process where an individual is repeatedly classified on a binary outcome variable (e.g., sick or well on occasion t), or in "multilevel" or "clustered" problems in which individuals within g...

Bayesian model selection for logistic regression models with random intercept

Data, collected to model risk of an interesting event, often have a multilevel structure as patients are clustered within larger units, e.g. clinical centers. Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters. Model specification requires to decide which regressors have a non-negligible effect, and hence, ...

Random intercept selection in structured additive regression models

review of radiographic methods in dental implantology

چکیده ندارد.