gamlj: GAMLj Suite for linear models

#' Beers at bars
#'
#' Simulated data testing random coefficients regression, with smiles as dependent
#' variable, beers as independent variable, and bars as cluster
#'
#' @docType data
#' @name beers_bars
#' @usage data(beers_bars)
#' @keywords datasets
#' @examples
#' data(beers_bars)
NULL

#' Subjects by stimuli cross-classification dataset
#'
#' Simulated data for subjects by stimuli cross-classification examples
#'
#' @docType data
#' @name subjects_by_stimuli
#' @usage data(subjects_by_stimuli)
#' @keywords datasets
#' @examples
#' data(subjects_by_stimuli)
NULL

#' Subjects by stimuli nested classification dataset
#'
#' Simulated data for stimuli nested in subjects classification examples
#'
#' @docType data
#' @name subjects_on_stimuli
#' @usage data(subjects_on_stimuli)
#' @keywords datasets
#' @examples
#' data(subjects_on_stimuli)
NULL


#' Sport data with curvilinear effects
#'
#' Simulated data for curvilinear effects examples
#'
#' @docType data
#' @name qsport
#' @usage data(qsport)
#' @keywords datasets
#' @examples
#' data(qsport)
NULL

#' Depression over time
#'
#' Data repeated measure anova with mixed models
#'
#' @docType data
#' @name wicksell
#' @usage data(wicksell)
#' @keywords datasets
#' @references David C. Howell, Overview of Mixed Models \url{https://www.uvm.edu/~statdhtx/StatPages/Mixed-Models-Repeated/Mixed-Models-Overview.html}
#' @examples
#' data(wicksell)
NULL

#' Five groups for contrasts
#'
#' Simulated data with five groups and a continuous dependent variable for checking contrasts results
#'
#' @docType data
#' @name fivegroups
#' @usage data(fivegroups)
#' @keywords datasets
#' @examples
#' data(fivegroups)
NULL



#' Many Models Data
#'
#' Generated data to test linear models. The variables can be used as:
#'       
#'       `x` and `z` as two continuous independent variables; 
#'       
#'       `cat2` and `cat3`  as categorical independent variables, with two and three groups respectively;
#'       
#'       `ycont` is a continuous variable, suitable as dependent variable. 
#'       `ybin` is dichotomous dependent variable; `ypoi` a count variable (Poisson),
#'       `yord` and ordinal dependent variable with 5 levels, and `ycat` a categorical dependent variable with three groups.

#' @docType data
#' @name manymodels
#' @usage data(manymodels)
#' @keywords datasets
#' @examples
#' data(manymodels)
NULL


#' Many Mixed Models
#'
#'       Generated data to test different types of linear models with clustered data. 
#'       The variables can be used as:
#'       
#'       `x` and `z` as two continuous independent variables; 
#'       
#'       `cat2` and `cat3`  as categorical independent variables, with two and three groups respectively;
#'       
#'       `ycont` is a continuous variable, suitable as dependent variable. 
#'       `ybin` is dichotomous dependent variable; `ypoi` a count variable (Poisson),
#'       `yord` and ordinal dependent variable with 5 levels, and `ycat` a categorical dependent variable with three groups.

#'       `cluster` as the clustering variable.
#' @docType data
#' @name clustermanymodels
#' @usage data(clustermanymodels)
#' @keywords datasets
#' @examples
#' data(clustermanymodels)
NULL


#' School data for testing
#'
#' Data for different examples
#'
#' @docType data
#' @name hsbdemo
#' @usage data(hsbdemo)
#' @keywords datasets
#' @examples
#' data(hsbdemo)
NULL


#' Poisson data
#'
#'  Simulated data of a poisson distributed dependent variables with some independent variabes
#'
#' @docType data
#' @name poissonacts
#' @usage data(poissonacts)
#' @keywords datasets
#' @examples
#' data(poissonacts)
NULL