lsttheory: Latent State-Trait Theory

##
## These "global" variables are used in subset() or dplyr commands and are
## mistakenly classified as global variables by R CMD Check. Therefore they
## are added here as "global" variables to avoid the NOTE.
utils::globalVariables(c("est", "gamma_t_equiv", "label", "lhs",
                         "manifest_thetacovariates", "op"))

#' lsttheory
#' 
#' Compute several models of latent state-trait theory..
#' _PACKAGE


#' Dataset d_taucong.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y1.
#'   \item y2.
#'   \item y3.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 300 rows and 3 variables
#' @name d_taucong
NULL


#' Dataset d_multitraitmultistate02.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#'   \item y13.
#'   \item y23.
#'   \item y14.
#'   \item y24.
#'   \item x.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 9 variables
#' @name d_multitraitmultistate02
NULL



#' Dataset d_multitraitmultistate.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#'   \item y13.
#'   \item y23.
#'   \item y14.
#'   \item y24.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 8 variables
#' @name d_multitraitmultistate
NULL



#' Dataset d_multistate02.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y31.
#'   \item y12.
#'   \item y22.
#'   \item y32.
#'   \item y13.
#'   \item y23.
#'   \item y33.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 1000 rows and 9 variables
#' @name d_multistate02
NULL



#' Dataset d_multistate.
#' 
#' A simulated dataset. The variables are:
#' 
#' \itemize{
#'   \item y11.
#'   \item y21.
#'   \item y12.
#'   \item y22.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 400 rows and 4 variables
#' @name d_multistate
NULL



#' Dataset mmLSTrf_RealDataExample
#'
#' This dataset is a subsample from a larger longitudinal study assessing the 
#' Big Five personality traits in both offline (real-world) and online (digital) 
#' contexts. Data were collected using the self- and other-rating versions of 
#' the Big Five Inventory-2 (BFI-2; Soto and John, 2017b) at two measurement 
#' occasions, with an average interval of 11 days.
#' 
#' From this broader study, six self-rated items measuring the dimension of 
#' Negative Emotionality (N) were selected. These chosen items correspond to 
#' those of the short version of the BFI-2 (BFI-2-S; Soto and John, 2017a) and 
#' are rated on a five-point Likert scale. Three of the items are true-keyed, 
#' and three are false-keyed.
#'
#' The six items are listed below; the false-keyed are marked with “*”.
#' 
#' \itemize{
#'   \item NAt. Worries a lot. 
#'   \item NDt. Tends to feel depressed, blue.
#'   \item NVt. Is temperamental, gets emotional easily. 
#'   \item NAf. Is relaxed, handles stress well*. 
#'   \item NDf. Feels secure, comfortable with self*.
#'   \item NVf. Is emotionally stable, not easily upset*. 
#' }
#' 
#' Each item exists in four versions in the dataset, resulting in a total 
#' of 24 variables. Items assessing Negative Emotionality in the offline 
#' context are labeled with the prefixes “Of1_” (measurement occasion 
#' one) and “Of2_” (measurement occasion two), while those in the online 
#' context are labeled “On1_” and “On2_” respectively.
#' 
#' The resulting dataset reflects a study design with two fixed situations 
#' (offline and online), two measurement occasions, two measurement methods 
#' (true- and false-keyed), and three indicators per method (true-keyed: 
#' NVt, NAt, NDt; false-keyed: NVf, NAf, NDf).
#' 
#' Note: The dataset contains missing values.
#'
#' @docType data
#' @keywords datasets
#' @format A data frame with 425 rows and 24 variables
#' @name mmLSTrf_RealDataExample
NULL



#' Dataset mmLSTrf_SimulatedDataExample.
#' 
#' A simulated dataset illustrating data for a study design with two fixed 
#' situations, three measurement occasions, two methods and three indicators. 
#' 
#' The variables are named according to the following format: \eqn{Y_{imts}}
#' (i = indicator, m = method, t = occasion, s = fixed situation)
#' \itemize{
#'   \item \eqn{Y_{1111}}
#'   \item \eqn{Y_{2111}}
#'   \item \eqn{Y_{3111}}
#'   \item \eqn{Y_{1211}}
#'   \item \eqn{Y_{2211}}
#'   \item ...
#'   \item \eqn{Y_{2132}}
#'   \item \eqn{Y_{3132}}
#'   \item \eqn{Y_{1132}}
#'   \item \eqn{Y_{2232}}
#'   \item \eqn{Y_{3232}}
#' }
#' 
#' This format reflects the order of indicator variables in a path diagram, where
#' indicators are first grouped by fixed situations, within those they are then
#' grouped by occasions and within those they are lastly grouped by methods. 
#' The resulting nested structure has indicators nested within methods, nested
#' within occasions, nested within fixed situations.
#' 
#' The specified population values underlying the simulated data are: 
#' \itemize{
#'   \item \eqn{E(T_{111})} = 2.75
#'   \item \eqn{Comm(T_{112})} = 0.44
#'   \item \eqn{E(T_{112})} = 3.25
#'   \item \eqn{\epsilon_{imts}} = 0.15
#'   \item \eqn{Var(T_{111})} = 0.40
#'   \item \eqn{\alpha_{ims}} = 0.00
#'   \item \eqn{Var(T_{112})} = 0.45
#'   \item \eqn{\lambda_{ims}} = 1.00
#'   \item \eqn{Var(O_{11t1})} = 0.20
#'   \item \eqn{\delta_{ims}} = 1.00
#'   \item \eqn{Var(O_{11t2})} = 0.30
#'   \item \eqn{\gamma_{ims}} = 1.00
#'   \item \eqn{Var(TM_{ims})} = 0.10
#'   \item \eqn{\beta_{1112}}* = 0.35
#'   \item \eqn{Var(OM_{mts})} = 0.10
#'   \item \eqn{\beta_{0112}} = 1.31
#'   \item \eqn{Var(\omega_{11s})} = 0.25
#'   }
#' *\eqn{\beta_{1112}} represents the standardized beta coefficient.
#' Trait factors are essentially parallel, other latent variables are essentially 
#' equivalent. Scalar measurement invariance holds across fixed situations and  
#' methods. Latent variables are orthogonal apart from trait factors.
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 500 rows and 36 variables
#' @name mmLSTrf_SimulatedDataExample
NULL



#' Dataset happy
#' 
#' Restructured wide-format data from Weiss et al. (2021), containing the items 
#' happiness ("How happy do you feel at the moment") and 
#' life satisfaction ("How satisfied are you with your life at the moment?") on five-point Likert scales.
#' Data was assessed 5 times a day on 5 days during an experience-sampling study.
#' The original dataset is available online at: https://osf.io/kwp6n/ 
#' The variables are:
#' 
#' \itemize{
#'   \item happy_1. Self-reported happiness on occasion 1. 
#'   \item satisfaction_1. Self-reported life satisfaction on occasion 1.
#'   \item happy_2. Self-reported happiness on occasion 2.
#'   \item satisfaction_2. Self-reported life satisfaction on occasion 2.
#'   \item ...
#'   \item happy_25. Self-reported happiness on occasion 25.
#'   \item satisfaction_25. Self-reported life satisfaction on occasion 25.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 425 rows and 50 variables
#' @name happy
NULL



#' Dataset happybig5
#' 
#' Restructured wide-format data from Weiss et al. (2021), containing the items 
#' happiness ("How happy do you feel at the moment") and 
#' life satisfaction ("How satisfied are you with your life at the moment?") on five-point Likert scales.
#' Data was assessed 5 times a day on 5 days during an experience-sampling study.
#' Additionally, this dataset contains measures of the Big Five personality dimensions from an intake session before the experience sampling phase.
#' The Big Five personality dimensions reflect mean values of 2 items each, one of which reverse coded (Gosling et al., 2003; Muck et al., 2007)
#' This dataset can be used to demonstrate how covariates (Big Five) contribute to trait components of Latent State-Trait models. 
#' The original dataset (including codebooks) is available online at: https://osf.io/kwp6n/ 
#' The variables are:
#' 
#' \itemize{
#'   \item happy_1. Self-reported happiness on occasion 1. 
#'   \item satisfaction_1. Self-reported life satisfaction on occasion 1.
#'   \item happy_2. Self-reported happiness on occasion 2.
#'   \item satisfaction_2. Self-reported life satisfaction on occasion 2.
#'   \item ...
#'   \item happy_25. Self-reported happiness on occasion 25.
#'   \item satisfaction_25. Self-reported life satisfaction on occasion 25.
#'   \item Big5_OE_M. Openness.
#'   \item Big5_CO_M. Conscientiousness.
#'   \item Big5_EX_M. Extraversion.
#'   \item Big5_AG_M. Agreeableness.
#'   \item Big5_ES_M. Emotional Stability.
#' }
#' 
#' @docType data
#' @keywords datasets
#' @format A data frame with 425 rows and 55 variables
#' @name happybig5
NULL


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

#' @importFrom utils head tail
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#' @importFrom magrittr %>%
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