nlpsem (version 0.3)

getLCSM: Fit a Latent Change Score Model with a Time-invariant Covariate (If Any)

Description

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.

Usage

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
)

Value

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.

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.

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")

Examples

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)

# \donttest{
# 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
  )
# }