# Fit a Multivariate Latent Growth Curve Model or Multivariate Latent Change Score Model This function fits a multivariate latent growth curve model or a multivariate latent change score model with the provided data. It manages model setup, optimization, and if requested, outputs parameter estimates and standard errors. ```r getMGM( dat, t_var, y_var, curveFun, intrinsic = TRUE, records, y_model, starts = NULL, res_scale = NULL, res_cor = 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 for multiple longitudinal outcomes. - `t_var`: A vector of strings, with each element representing the prefix for column names related to the time variable for the corresponding outcome variable at each study wave. - `y_var`: A vector of strings, with each element representing the prefix for column names corresponding to a particular outcome variable at each study wave. - `curveFun`: A string specifying the functional forms of the growth curve(s). Supported options for `y_model = "LGCM"` include: `"linear"` (or `"LIN"`), `"quadratic"` (or `"QUAD"`), `"negative exponential"` (or `"EXP"`), `"Jenss-Bayley"` (or `"JB"`), and `"bilinear spline"` (or `"BLS"`). Supported options for `y_model = "LCSM"` include: `"nonparametric"` (or `"NonP"`), `"quadratic"` (or `"QUAD"`), `"negative exponential"` (or `"EXP"`), and `"Jenss-Bayley"` (or `"JB"`). - `intrinsic`: A logical flag indicating whether to build an intrinsically nonlinear longitudinal model. Default is `TRUE`. - `records`: A list of numeric vectors, with each vector specifying the indices of the observed study waves for the corresponding outcome variable. - `y_model`: A string specifying how to fit the longitudinal outcome. Supported values are `"LGCM"` and `"LCSM"`. - `starts`: A list containing initial values for the parameters. Default is `NULL`, indicating no user-specified initial values. - `res_scale`: A numeric vector with each element representing the scaling factor for the initial calculation of the residual variance. These values 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. - `res_cor`: A numeric value or vector for user-specified residual correlation between any two longitudinal outcomes to calculate the corresponding initial value. 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 multivariate latent growth curve model or a multivariate 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$T1 - baseT RMS_dat0$T2 <- RMS_dat0$T2 - baseT RMS_dat0$T3 <- RMS_dat0$T3 - baseT RMS_dat0$T4 <- RMS_dat0$T4 - baseT RMS_dat0$T5 <- RMS_dat0$T5 - baseT RMS_dat0$T6 <- RMS_dat0$T6 - baseT RMS_dat0$T7 <- RMS_dat0$T7 - baseT RMS_dat0$T8 <- RMS_dat0$T8 - baseT RMS_dat0$T9 <- RMS_dat0$T9 - baseT # Fit linear multivariate latent growth curve model LIN_PLGCM_f <- getMGM( dat = RMS_dat0, t_var = c("T", "T"), y_var = c("R", "M"), curveFun = "LIN", intrinsic = FALSE, records = list(1:9, 1:9), y_model = "LGCM", res_scale = c(0.1, 0.1), res_cor = 0.3 ) # Fit bilinear spline multivariate latent growth curve model (random knots) ## Define parameter names paraBLS_PLGCM.f <- c( "Y_mueta0", "Y_mueta1", "Y_mueta2", "Y_knot", paste0("Y_psi", c("00", "01", "02", "0g", "11", "12", "1g", "22", "2g", "gg")), "Y_res", "Z_mueta0", "Z_mueta1", "Z_mueta2", "Z_knot", paste0("Z_psi", c("00", "01", "02", "0g", "11", "12", "1g", "22", "2g", "gg")), "Z_res", paste0("YZ_psi", c(c("00", "10", "20", "g0", "01", "11", "21", "g1", "02", "12", "22", "g2", "0g", "1g", "2g", "gg"))),"YZ_res" ) ## Fit model BLS_PLGCM_f <- getMGM( dat = RMS_dat0, t_var = c("T", "T"), y_var = c("R", "M"), curveFun = "BLS", intrinsic = TRUE, records = list(1:9, 1:9), y_model = "LGCM", res_scale = c(0.1, 0.1), res_cor = 0.3, paramOut = TRUE, names = paraBLS_PLGCM.f ) printTable(BLS_PLGCM_f) ``` ## References * Liu, J., & Perera, R. A. (2021). "Estimating Knots and Their Association in Parallel Bilinear Spline Growth Curve Models in the Framework of Individual Measurement Occasions," Psychological Methods (Advance online publication). tools:::Rd_expr_doi("10.1037/met0000309") * Blozis, S. A. (2004). "Structured Latent Curve Models for the Study of Change in Multivariate Repeated Measures," Psychological Methods, 9(3), 334-353. tools:::Rd_expr_doi("10.1037/1082-989X.9.3.334")

getMGM function