# Fit a Latent Change Score Model with a Time-invariant Covariate (If Any) This function fits a latent change score model with or without time-invariant covariates to the provided data. It manages model setup, optimization, and if requested, outputs parameter estimates and standard errors. ```r getLCSM( dat, t_var, y_var, curveFun, intrinsic = TRUE, records, growth_TIC = NULL, starts = NULL, res_scale = NULL, tries = NULL, OKStatus = 0, jitterD = "runif", loc = 1, scale = 0.25, paramOut = FALSE, names = NULL ) ``` ## Arguments - `dat`: A wide-format data frame, with each row corresponding to a unique ID. It contains the observed variables with repeated measurements and occasions, and time-invariant covariates (TICs) if any. - `t_var`: A string specifying the prefix of the column names corresponding to the time variable at each study wave. - `y_var`: A string specifying the prefix of the column names corresponding to the outcome variable at each study wave. - `curveFun`: A string specifying the functional form of the growth curve. Supported options for latent change score models include: `"quadratic"` (or `"QUAD"`), `"negative exponential"` (or `"EXP"`), `"Jenss-Bayley"` (or `"JB"`), and `"nonparametric"` (or `"NonP"`). - `intrinsic`: A logical flag indicating whether to build an intrinsically nonlinear longitudinal model. Default is `TRUE`. - `records`: A numeric vector specifying indices of the study waves. - `growth_TIC`: A string or character vector specifying the column name(s) of time-invariant covariate(s) contributing to the variability of growth factors if any. Default is `NULL`, indicating no growth TICs are included in the model. - `starts`: A list containing initial values for the parameters. Default is `NULL`, indicating no user-specified initial values. - `res_scale`: A numeric value representing the scaling factor for the initial calculation of the residual variance. This value should be between `0` and `1`, exclusive. By default, this is `NULL`, as it is unnecessary when the user specifies the initial values using the `starts` argument. - `tries`: An integer specifying the number of additional optimization attempts. Default is `NULL`. - `OKStatus`: An integer (vector) specifying acceptable status codes for convergence. Default is `0`. - `jitterD`: A string specifying the distribution for jitter. Supported values are: `"runif"` (uniform distribution), `"rnorm"` (normal distribution), and `"rcauchy"` (Cauchy distribution). Default is `"runif"`. - `loc`: A numeric value representing the location parameter of the jitter distribution. Default is `1`. - `scale`: A numeric value representing the scale parameter of the jitter distribution. Default is `0.25`. - `paramOut`: A logical flag indicating whether to output the parameter estimates and standard errors. Default is `FALSE`. - `names`: A character vector specifying parameter names. Default is `NULL`. ## Returns An object of class `myMxOutput`. Depending on the `paramOut` argument, the object may contain the following slots: * `mxOutput`: This slot contains the fitted latent change score model. A summary of this model can be obtained using the `ModelSummary()` function. * `Estimates` (optional): If `paramOut = TRUE`, a data frame with parameter estimates and standard errors. The content of this slot can be printed using the `printTable()` method for S4 objects. ## Examples ```r mxOption(model = NULL, key = "Default optimizer", "CSOLNP", reset = FALSE) # Load ECLS-K (2011) data data("RMS_dat") RMS_dat0 <- RMS_dat # Re-baseline the data so that the estimated initial status is for the starting point of the study baseT <- RMS_dat0$T1 RMS_dat0$T1 <- (RMS_dat0$T1 - baseT)/12 RMS_dat0$T2 <- (RMS_dat0$T2 - baseT)/12 RMS_dat0$T3 <- (RMS_dat0$T3 - baseT)/12 RMS_dat0$T4 <- (RMS_dat0$T4 - baseT)/12 RMS_dat0$T5 <- (RMS_dat0$T5 - baseT)/12 RMS_dat0$T6 <- (RMS_dat0$T6 - baseT)/12 RMS_dat0$T7 <- (RMS_dat0$T7 - baseT)/12 RMS_dat0$T8 <- (RMS_dat0$T8 - baseT)/12 RMS_dat0$T9 <- (RMS_dat0$T9 - baseT)/12 # Standardized time-invariant covariates RMS_dat0$ex1 <- scale(RMS_dat0$Approach_to_Learning) RMS_dat0$ex2 <- scale(RMS_dat0$Attention_focus) # Fit nonparametric change score model for reading development ## Fit model NonP_LCSM <- getLCSM( dat = RMS_dat0, t_var = "T", y_var = "R", curveFun = "nonparametric", intrinsic = FALSE, records = 1:9, res_scale = 0.1 ) ``` ## References * Liu, J., & Perera, R. A. (2023). Estimating Rate of Change for Nonlinear Trajectories in the Framework of Individual Measurement Occasions: A New Perspective on Growth Curves. Behavior Research Methods. tools:::Rd_expr_doi("10.3758/s13428-023-02097-2") * Liu, J. (2022). "Jenss–Bayley Latent Change Score Model With Individual Ratio of the Growth Acceleration in the Framework of Individual Measurement Occasions." Journal of Educational and Behavioral Statistics, 47(5), 507–543. tools:::Rd_expr_doi("10.3102/10769986221099919") * Grimm, K. J., Zhang, Z., Hamagami, F., & Mazzocco, M. (2013). "Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories." Multivariate Behavioral Research, 48(1), 117-143. tools:::Rd_expr_doi("10.1080/00273171.2012.755111")

getLCSM function