R/lsttheory_es.R
In amayer2010/lsttheory: Latent State-Trait Theory
Defines functions
lsttheory_es
Documented in
lsttheory_es
#' Estimate latent state-trait models for experience sampling data
#'
#' @author Julia Norget
#' @param model integer or character. Can be 1-9 or one of the following: c("MSSTinvar", "STARinvar", "PTARinvar", "ITARinvar", "PITARinvar", "STAR2", "PTAR2", "ITAR2", "PITAR2"). Each model implies invariance assumptions, but details can be changed with the options below.
#' \enumerate{
#' \item Model 1 ("STinvar" or "MSSTinvar") is a multistate-singletrait model (no autoregression). Models 1-5 assume state (residual)- and trait-equivalence, i.e., all factor loadings are fixed to 1 and all intercepts are fixed to 0.
#' \item Model 2 ("STARinvar") is a singletrait model with autoregression.
#' \item Model 3 ("PTARinvar") is a model with period-specific (usually day-specific) traits.
#' \item Model 4 ("ITARinvar") is a model with indicator-specific traits.
#' \item Model 5 ("PITARinvar") is a model with period- and indicator-specific traits.
#' \item Models 6 - 9 ("STAR2", "PTAR2", "ITAR2", "PITAR2") are the same as models 2-5, but with state (residual)-congenericity and measurement invariance within periods. In experience sampling data, periods usually correspond to days.
#'}
#' @param ntimepoints integer. The total number of measurement occasions on which data was collected.
#' @param nperiods integer. The number of periods (e.g. days or weeks) on which data was collected.
#' @param data a data.frame. This data frame contains the observed variables, sorted by time t and then
#' by indicator i, i.e., Y11, Y21, Y31, ... Y12, Y22, Y32 ... Y15, Y25, Y35 ... etc.
#' @param addsyntax character string. Will be added to generated lavaan syntax.
#' @param la_t_equiv Character. Invariance option for factor loadings of the latent trait.
#' Can be one of \code{c("one", "period.invar", "free")}.
#' @param la_o_equiv Character. Invariance option for factor loadings of the occasion factor (OCC).
#' Can be one of \code{c("one", "invar", "period.invar", "free")}.
#' In models without autoregression this corresponds to the factor loadings
#' of the state variables (for models with a single trait or period-specific traits) or state residual variables (for models with indicator-specific traits).
#' @param la_s_equiv Character. Invariance option for autoregression between occasion factors. Can be one of \code{c("zero", "invar", "interval.invar", "free")}.
#' @param vzeta_eqiv Character. Invariance option for variances of the state residual (zeta) variables. Can be one of \code{c("invar", "period.invar", "free")}.
#' @param veps_equiv Character. Invariance option for variances of the residual (epsilon) variables.
#' Can be one of \code{c("invar", "time.invar", "indicator.invar", "period.invar", "free")}.
#' @param vtheta_equiv Character. Invariance option for variances of the latent trait. Can be one of \code{c("invar","indicator.invar", "free")}.
#' @param nu_equiv Character. Invariance option for intercepts of the indicators. Can be one of \code{c("zero","period.invar", "free")}.
#' @param alpha_equiv Character. Invariance option for intercepts of the latent states.
#' Only relevant for models with a single trait or period-specific traits.
#' Can be one of \code{c("zero","period.invar", "free")}.
#' @param mtheta_equiv Character. Invariance option for means of the latent traits. Character. Can be one of \code{c("invar","indicator.invar", "free")}.
#' @param gamma_t_equiv Character. Invariance option for regression coefficient from covariates to the latent trait variables.
#' @param manifest_thetacovariates Vector or single character. Name or the variable (or variables) in the dataset which are covariates that further explain the trait variables in the model.
#' @param ... Further arguments passed to lower-level functions
#' @export
lsttheory_es <- function(model, ntimepoints, nperiods = 1, data, addsyntax = "",
la_t_equiv = NULL, la_o_equiv = NULL, la_s_equiv = NULL, vzeta_eqiv = NULL, veps_equiv = NULL,
vtheta_equiv = NULL, nu_equiv = NULL, alpha_equiv = NULL, mtheta_equiv = NULL,
gamma_t_equiv= NULL, manifest_thetacovariates = NULL, ...){
if(ntimepoints %% nperiods != 0){
stop("ntimepoints must be a multiple of nperiods")
}
if((ncol(data) - length(manifest_thetacovariates)) %% ntimepoints != 0){
stop("The number of variables (excluding covariates) is not a multiple of ntimepoints. Does your dataset include variables which are not part of the model?")
}
userdefined <- c(la_t_equiv = la_t_equiv, la_o_equiv = la_o_equiv, la_s_equiv = la_s_equiv, vzeta_eqiv = vzeta_eqiv, veps_equiv = veps_equiv,
vtheta_equiv = vtheta_equiv, nu_equiv = nu_equiv, alpha_equiv = alpha_equiv, mtheta_equiv = mtheta_equiv)
if(!is.null(alpha_equiv) & model %in% c(4,5,8,9, "ITARinvar", "PITARinvar", "ITAR2", "PITAR2")){
message("alpha_equiv option will be ignored. Indicator-specific LST-R models are formulated as first-order models and do not include intercepts for the latent states.")
}
if(model == 1 || model == "base" || model == "MSSTinvar" || model == "STinvar"){ # singletrait model, no autoregression
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "zero"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 2 || model == "STARinvar"){ # singletrait model with autoregression -- STARinvar
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 3 || model == "PTARinvar"){ # period-specific traits with autoregression -- PTARinvar
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 4 || model == "ITARinvar"){ # indicator-specific traits with autoregression -- ITARinvar
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 5 || model == "PITARinvar"){ # period- and indicator-specific traits with autoregression -- PITARinvar
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 6 || model == "STAR2"){ # model 2 with state congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 2
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 2
veps_equiv = "period.invar" # change from model 2
vtheta_equiv = "invar"
nu_equiv = "period.invar" # change from model 2
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 7 || model == "PTAR2"){ # model 3 with state congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 3
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 3
veps_equiv = "period.invar" # change from model 3
vtheta_equiv = "invar"
nu_equiv = "period.invar" # change from model 2
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 8 || model == "ITAR2"){ # model 4 with state residual congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 4
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 3
veps_equiv = "period.invar" # change from model 3
vtheta_equiv = "indicator.invar"
nu_equiv = "zero"
mtheta_equiv = "indicator.invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 9 || model == "PITAR2"){ # model 5 with state residual congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 5
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 5
veps_equiv = "period.invar" # change from model 5
vtheta_equiv = "indicator.invar" # change from model 5
nu_equiv = "zero"
mtheta_equiv = "indicator.invar"
gamma_t_equiv = "indicator.invar"
}
# overwrite the invariance options implied by models 1-9 if the user specifically defined these options
if(!is.null(userdefined)){
if(!is.na(userdefined["la_t_equiv"])){la_t_equiv = unname(userdefined["la_t_equiv"])}
if(!is.na(userdefined["la_o_equiv"])){la_o_equiv = unname(userdefined["la_o_equiv"])}
if(!is.na(userdefined["la_s_equiv"])){la_s_equiv = unname(userdefined["la_s_equiv"])}
if(!is.na(userdefined["vzeta_eqiv"])){vzeta_eqiv = unname(userdefined["vzeta_eqiv"])}
if(!is.na(userdefined["veps_equiv"])){veps_equiv = unname(userdefined["veps_equiv"])}
if(!is.na(userdefined["vtheta_equiv"])){vtheta_equiv = unname(userdefined["vtheta_equiv"])}
if(!is.na(userdefined["nu_equiv"])){nu_equiv = unname(userdefined["nu_equiv"])}
if(!is.na(userdefined["alpha_equiv"])){alpha_equiv = unname(userdefined["alpha_equiv"])}
if(!is.na(userdefined["mtheta_equiv"])){mtheta_equiv = unname(userdefined["mtheta_equiv"])}
}
if(model %in% c(1,2,3,6,7, "base", "MSSTinvar", "STinvar", "STARinvar", "PTARinvar", "STAR2", "PTAR2")){
res <- lst_models_es_common_trait(
ntimepoints=ntimepoints,
data=data,
addsyntax=addsyntax,
ntraitperiods=ntraitperiods,
nzetaperiods=nzetaperiods,
nepsperiods=nepsperiods,
la_t_equiv=la_t_equiv,
la_o_equiv=la_o_equiv,
la_s_equiv=la_s_equiv,
vzeta_eqiv=vzeta_eqiv,
veps_equiv=veps_equiv,
vtheta_equiv=vtheta_equiv,
nu_equiv=nu_equiv,
alpha_equiv=alpha_equiv,
mtheta_equiv=mtheta_equiv,
gamma_t_equiv=gamma_t_equiv,
manifest_thetacovariates=manifest_thetacovariates,
...)
}else if(model %in% c(4,5,8,9, "ITARinvar", "PITARinvar", "ITAR2", "PITAR2")){
res <- lst_models_es_indicator_specific_trait(
ntimepoints=ntimepoints,
data=data,
addsyntax=addsyntax,
ntraitperiods=ntraitperiods,
nzetaperiods=nzetaperiods,
nepsperiods=nepsperiods,
la_t_equiv=la_t_equiv,
la_o_equiv=la_o_equiv,
la_s_equiv=la_s_equiv,
vzeta_eqiv=vzeta_eqiv,
veps_equiv=veps_equiv,
vtheta_equiv=vtheta_equiv,
nu_equiv=nu_equiv,
#alpha_equiv=alpha_equiv,
mtheta_equiv=mtheta_equiv,
gamma_t_equiv=gamma_t_equiv,
manifest_thetacovariates=manifest_thetacovariates,
...)
}
return(res)
}
amayer2010/lsttheory documentation built on Nov. 3, 2023, 1:30 a.m.
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Defines functions lsttheory_es
Documented in lsttheory_es
#' Estimate latent state-trait models for experience sampling data
#'
#' @author Julia Norget
#' @param model integer or character. Can be 1-9 or one of the following: c("MSSTinvar", "STARinvar", "PTARinvar", "ITARinvar", "PITARinvar", "STAR2", "PTAR2", "ITAR2", "PITAR2"). Each model implies invariance assumptions, but details can be changed with the options below.
#' \enumerate{
#' \item Model 1 ("STinvar" or "MSSTinvar") is a multistate-singletrait model (no autoregression). Models 1-5 assume state (residual)- and trait-equivalence, i.e., all factor loadings are fixed to 1 and all intercepts are fixed to 0.
#' \item Model 2 ("STARinvar") is a singletrait model with autoregression.
#' \item Model 3 ("PTARinvar") is a model with period-specific (usually day-specific) traits.
#' \item Model 4 ("ITARinvar") is a model with indicator-specific traits.
#' \item Model 5 ("PITARinvar") is a model with period- and indicator-specific traits.
#' \item Models 6 - 9 ("STAR2", "PTAR2", "ITAR2", "PITAR2") are the same as models 2-5, but with state (residual)-congenericity and measurement invariance within periods. In experience sampling data, periods usually correspond to days.
#'}
#' @param ntimepoints integer. The total number of measurement occasions on which data was collected.
#' @param nperiods integer. The number of periods (e.g. days or weeks) on which data was collected.
#' @param data a data.frame. This data frame contains the observed variables, sorted by time t and then
#' by indicator i, i.e., Y11, Y21, Y31, ... Y12, Y22, Y32 ... Y15, Y25, Y35 ... etc.
#' @param addsyntax character string. Will be added to generated lavaan syntax.
#' @param la_t_equiv Character. Invariance option for factor loadings of the latent trait.
#' Can be one of \code{c("one", "period.invar", "free")}.
#' @param la_o_equiv Character. Invariance option for factor loadings of the occasion factor (OCC).
#' Can be one of \code{c("one", "invar", "period.invar", "free")}.
#' In models without autoregression this corresponds to the factor loadings
#' of the state variables (for models with a single trait or period-specific traits) or state residual variables (for models with indicator-specific traits).
#' @param la_s_equiv Character. Invariance option for autoregression between occasion factors. Can be one of \code{c("zero", "invar", "interval.invar", "free")}.
#' @param vzeta_eqiv Character. Invariance option for variances of the state residual (zeta) variables. Can be one of \code{c("invar", "period.invar", "free")}.
#' @param veps_equiv Character. Invariance option for variances of the residual (epsilon) variables.
#' Can be one of \code{c("invar", "time.invar", "indicator.invar", "period.invar", "free")}.
#' @param vtheta_equiv Character. Invariance option for variances of the latent trait. Can be one of \code{c("invar","indicator.invar", "free")}.
#' @param nu_equiv Character. Invariance option for intercepts of the indicators. Can be one of \code{c("zero","period.invar", "free")}.
#' @param alpha_equiv Character. Invariance option for intercepts of the latent states.
#' Only relevant for models with a single trait or period-specific traits.
#' Can be one of \code{c("zero","period.invar", "free")}.
#' @param mtheta_equiv Character. Invariance option for means of the latent traits. Character. Can be one of \code{c("invar","indicator.invar", "free")}.
#' @param gamma_t_equiv Character. Invariance option for regression coefficient from covariates to the latent trait variables.
#' @param manifest_thetacovariates Vector or single character. Name or the variable (or variables) in the dataset which are covariates that further explain the trait variables in the model.
#' @param ... Further arguments passed to lower-level functions
#' @export
lsttheory_es <- function(model, ntimepoints, nperiods = 1, data, addsyntax = "",
la_t_equiv = NULL, la_o_equiv = NULL, la_s_equiv = NULL, vzeta_eqiv = NULL, veps_equiv = NULL,
vtheta_equiv = NULL, nu_equiv = NULL, alpha_equiv = NULL, mtheta_equiv = NULL,
gamma_t_equiv= NULL, manifest_thetacovariates = NULL, ...){
if(ntimepoints %% nperiods != 0){
stop("ntimepoints must be a multiple of nperiods")
}
if((ncol(data) - length(manifest_thetacovariates)) %% ntimepoints != 0){
stop("The number of variables (excluding covariates) is not a multiple of ntimepoints. Does your dataset include variables which are not part of the model?")
}
userdefined <- c(la_t_equiv = la_t_equiv, la_o_equiv = la_o_equiv, la_s_equiv = la_s_equiv, vzeta_eqiv = vzeta_eqiv, veps_equiv = veps_equiv,
vtheta_equiv = vtheta_equiv, nu_equiv = nu_equiv, alpha_equiv = alpha_equiv, mtheta_equiv = mtheta_equiv)
if(!is.null(alpha_equiv) & model %in% c(4,5,8,9, "ITARinvar", "PITARinvar", "ITAR2", "PITAR2")){
message("alpha_equiv option will be ignored. Indicator-specific LST-R models are formulated as first-order models and do not include intercepts for the latent states.")
}
if(model == 1 || model == "base" || model == "MSSTinvar" || model == "STinvar"){ # singletrait model, no autoregression
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "zero"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 2 || model == "STARinvar"){ # singletrait model with autoregression -- STARinvar
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 3 || model == "PTARinvar"){ # period-specific traits with autoregression -- PTARinvar
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 4 || model == "ITARinvar"){ # indicator-specific traits with autoregression -- ITARinvar
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 5 || model == "PITARinvar"){ # period- and indicator-specific traits with autoregression -- PITARinvar
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "one"
la_s_equiv = "invar"
vzeta_eqiv = "invar"
veps_equiv = "invar"
vtheta_equiv = "invar"
nu_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 6 || model == "STAR2"){ # model 2 with state congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 2
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 2
veps_equiv = "period.invar" # change from model 2
vtheta_equiv = "invar"
nu_equiv = "period.invar" # change from model 2
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 7 || model == "PTAR2"){ # model 3 with state congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 3
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 3
veps_equiv = "period.invar" # change from model 3
vtheta_equiv = "invar"
nu_equiv = "period.invar" # change from model 2
alpha_equiv = "zero"
mtheta_equiv = "invar"
gamma_t_equiv = "invar"
}else if(model == 8 || model == "ITAR2"){ # model 4 with state residual congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = 1
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 4
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 3
veps_equiv = "period.invar" # change from model 3
vtheta_equiv = "indicator.invar"
nu_equiv = "zero"
mtheta_equiv = "indicator.invar"
gamma_t_equiv = "indicator.invar"
}else if(model == 9 || model == "PITAR2"){ # model 5 with state residual congenericity and measurement invariance within each period (e.g. day)
ntraitperiods = nperiods
nzetaperiods = nperiods
nepsperiods = nperiods
la_t_equiv = "one"
la_o_equiv = "period.invar" # change from model 5
la_s_equiv = "invar"
vzeta_eqiv = "period.invar" # change from model 5
veps_equiv = "period.invar" # change from model 5
vtheta_equiv = "indicator.invar" # change from model 5
nu_equiv = "zero"
mtheta_equiv = "indicator.invar"
gamma_t_equiv = "indicator.invar"
}
# overwrite the invariance options implied by models 1-9 if the user specifically defined these options
if(!is.null(userdefined)){
if(!is.na(userdefined["la_t_equiv"])){la_t_equiv = unname(userdefined["la_t_equiv"])}
if(!is.na(userdefined["la_o_equiv"])){la_o_equiv = unname(userdefined["la_o_equiv"])}
if(!is.na(userdefined["la_s_equiv"])){la_s_equiv = unname(userdefined["la_s_equiv"])}
if(!is.na(userdefined["vzeta_eqiv"])){vzeta_eqiv = unname(userdefined["vzeta_eqiv"])}
if(!is.na(userdefined["veps_equiv"])){veps_equiv = unname(userdefined["veps_equiv"])}
if(!is.na(userdefined["vtheta_equiv"])){vtheta_equiv = unname(userdefined["vtheta_equiv"])}
if(!is.na(userdefined["nu_equiv"])){nu_equiv = unname(userdefined["nu_equiv"])}
if(!is.na(userdefined["alpha_equiv"])){alpha_equiv = unname(userdefined["alpha_equiv"])}
if(!is.na(userdefined["mtheta_equiv"])){mtheta_equiv = unname(userdefined["mtheta_equiv"])}
}
if(model %in% c(1,2,3,6,7, "base", "MSSTinvar", "STinvar", "STARinvar", "PTARinvar", "STAR2", "PTAR2")){
res <- lst_models_es_common_trait(
ntimepoints=ntimepoints,
data=data,
addsyntax=addsyntax,
ntraitperiods=ntraitperiods,
nzetaperiods=nzetaperiods,
nepsperiods=nepsperiods,
la_t_equiv=la_t_equiv,
la_o_equiv=la_o_equiv,
la_s_equiv=la_s_equiv,
vzeta_eqiv=vzeta_eqiv,
veps_equiv=veps_equiv,
vtheta_equiv=vtheta_equiv,
nu_equiv=nu_equiv,
alpha_equiv=alpha_equiv,
mtheta_equiv=mtheta_equiv,
gamma_t_equiv=gamma_t_equiv,
manifest_thetacovariates=manifest_thetacovariates,
...)
}else if(model %in% c(4,5,8,9, "ITARinvar", "PITARinvar", "ITAR2", "PITAR2")){
res <- lst_models_es_indicator_specific_trait(
ntimepoints=ntimepoints,
data=data,
addsyntax=addsyntax,
ntraitperiods=ntraitperiods,
nzetaperiods=nzetaperiods,
nepsperiods=nepsperiods,
la_t_equiv=la_t_equiv,
la_o_equiv=la_o_equiv,
la_s_equiv=la_s_equiv,
vzeta_eqiv=vzeta_eqiv,
veps_equiv=veps_equiv,
vtheta_equiv=vtheta_equiv,
nu_equiv=nu_equiv,
#alpha_equiv=alpha_equiv,
mtheta_equiv=mtheta_equiv,
gamma_t_equiv=gamma_t_equiv,
manifest_thetacovariates=manifest_thetacovariates,
...)
}
return(res)
}
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