R/00documentation.R

############# documentation ###############

#' EffectLiteR
#' 
#' Use structural equation modeling to estimate average and conditional effects 
#' of a treatment variable on an outcome variable, taking into account multiple
#' continuous and categorical covariates.
#'
#' @name EffectLiteR
#' @docType package
NULL


#' Dataset nonortho.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y. Continuous dependent variable depression.
#'   \item x. Treatment variable with values 0 (control), 1 (treat1), and 2 (treat2).
#'   \item z. Categorical covariate with values 0 (low neediness), 1 (medium neediness) and 2 (high neediness).
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 500 rows and 3 variables
#' @name nonortho
NULL



#' Dataset example01.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item x. Treatment variable with values control, treat1, and treat2.
#'   \item k1. Categorical covariate with values male and female.
#'   \item kateg2. Categorical covariate with values 1 and 2.
#'   \item z1-z3. Continuous covariates.
#'   \item dv. Coninuous dependent variable.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 2000 rows and 7 variables.
#' @name example01
NULL



#' Dataset example02lv.
#' 
#' A simulated dataset with latent variables. The variables are:
#' 
#' \itemize{
#'   \item CPM11. First indicator of latent covariate.
#'   \item CPM21. Second indicator of latent covariate.
#'   \item CPM12. First indicator of latent outcome.
#'   \item CPM22. Second indicator of latent outcome.
#'   \item x. Dichotomous treatment variable with values 0 (control), and 1 (treatment).
#'   \item k. Categorical covariate with values first, second, and third.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 300 rows and 6 variables.
#' @name example02lv
NULL


#' Dataset MDRS2016.
#' 
#' The simulated dataset with latent variables used in Mayer, Dietzfelbinger, Rosseel, and Steyer (2016). The variables are:
#' 
#' \itemize{
#'   \item y11. First indicator of latent covariate (pretest mental health).
#'   \item y21. Second indicator of latent covariate (pretest mental health).
#'   \item y31. Third indicator of latent covariate (pretest mental health).
#'   \item y12. First indicator of latent outcome (posttest mental health).
#'   \item y22. Second indicator of latent outcome (posttest mental health).
#'   \item y32. Third indicator of latent outcome (posttest mental health).
#'   \item x. Categorical treatment variable with values 0 (wait list control group), 1 (conventional therapy), and 2 (innovative therapy).
#'   \item k. Categorical covariate with values 0 (male) and 1 (female).
#'   \item Ix1. Binary indicator for conventional therapy (X=1).
#'   \item Ix2. Binary indicator for innovative therapy (X=2).
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 10 variables.
#' @name MDRS2016
NULL


#' Dataset example_multilevel.
#' 
#' A simulated dataset with a cluster ID and sampling weights to test multilevel options. The variables are:
#' 
#' \itemize{
#'   \item y. Coninuous dependent variable.
#'   \item x. Treatment variable with values 0, 1.
#'   \item z. Continuous covariate.
#'   \item xz. Product of x and z.
#'   \item cid. Cluster ID.
#'   \item weights. Sampling weights.
#'   \item iptw. Classic inverse probability of treatment weights based on a logistic regression of x on z. Use with care (only for average effects).
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 800 rows and 7 variables.
#' @name example_multilevel
NULL


#' Dataset sophonet_data_simulated.
#' 
#' A simulated dataset based on the SOPHONET-study (Leichsenring et al., 2013). The variables are:
#' 
#' \itemize{
#'   \item lsas.a.t2 
#'   \item lsas.v.t2 
#'   \item lsas.a.t1 
#'   \item lsas.v.t1 
#'   \item bdi.t1.i1 
#'   \item bdi.t1.i2 
#'   \item bdi.t1.i3 
#'   \item ecr.anx.t1.i1 
#'   \item ecr.anx.t1.i2 
#'   \item ecr.anx.t1.i3 
#'   \item ecr.avoi.t1.i1 
#'   \item ecr.avoi.t1.i2 
#'   \item ecr.avoi.t1.i3 
#'   \item tpq.ha.i1 
#'   \item tpq.ha.i2 
#'   \item tpq.ha.i3 
#'   \item tosca.shame.t1.i1 
#'   \item tosca.shame.t1.i2 
#'   \item fskn.se.t1.i1 
#'   \item fskn.se.t1.i2 
#'   \item comorbid 
#'   \item iip.lov 
#'   \item iip.dom tb
#' }
#' 
#' @docType data
#' @keywords datasets
#' @references Leichsenring, F., Salzer, S., Beutel, M. E., Herpertz, S., Hiller, W., Hoyer, J., Huesing, J., ..., Leibing, E. (2013). Psychodynamic therapy and cognitive-behavioral therapy in social anxiety disorder: A multicenter randomized controlled trial. American Journal of Psychiatry, 170, 759–767.
#' @format A data frame with 328 rows and 24 variables.
#' @name sophonet_data_simulated
NULL


#' Dataset elrdata_categorical_items.
#' 
#' A simulated dataset for testing measurement models with categorical items:
#' 
#' \itemize{
#'   \item x. Treatment variable with values 0, 1.
#'   \item z11. indicator for covariate.
#'   \item z21. indicator for covariate.
#'   \item z31. indicator for covariate.
#'   \item z41. indicator for covariate.
#'   \item z51. indicator for covariate.
#'   \item y11. indicator for outcome.
#'   \item y21. indicator for outcome.
#'   \item y31. indicator for outcome.
#'   \item y41. indicator for outcome.
#'   \item y51. indicator for outcome.
#'   \item y61. indicator for outcome.
#'   \item y71. indicator for outcome.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 10000 rows and 13 variables.
#' @name elrdata_categorical_items
NULL



#' Dataset elrdata_logreg.
#' 
#' A simulated dataset for testing logistic regression:
#' 
#' \itemize{
#'   \item y. Outcome variable with values 0, 1.
#'   \item x. Treatment variable with values 0, 1.
#'   \item z1. continuous covariate.
#'   \item z2. continuous covariate.
#'   \item k1. categorical covariate.
#'   \item k2. categorical covariate.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 10000 rows and 6 variables.
#' @name elrdata_logreg
NULL



############## namespace ###########

#' @importFrom methods new is
NULL

#' @importMethodsFrom methods show 
NULL

#' @importFrom stats as.formula ftable model.frame model.matrix pnorm relevel var qnorm cov lm mahalanobis pchisq pf pt sd na.omit
NULL

#' @importFrom utils capture.output read.csv read.csv2 read.table combn
NULL

Try the EffectLiteR package in your browser

Any scripts or data that you put into this service are public.

EffectLiteR documentation built on July 9, 2023, 7:02 p.m.