getMediation: Fit a Longitudinal Mediation Model

In nlpsem: Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Fit a Longitudinal Mediation Model

Description

This function fits a longitudinal mediation model to the provided data. It manages model setup, optimization, and if requested, outputs parameter estimates and standard errors.

Usage

getMediation(
  dat,
  t_var,
  y_var,
  m_var,
  x_type,
  x_var,
  curveFun,
  records,
  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 processes and a baseline predictor when applicable.
t_var	A vector of strings, with each element representing the prefix for column names related to the time variable for the corresponding longitudinal 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.
m_var	A string specifying the prefix of the column names corresponding to the mediator variable at each study wave.
x_type	A string indicating the type of predictor variable used in the model. Supported values are "baseline" and "longitudinal".
x_var	A string specifying the baseline predictor if x_type = "baseline", or the prefix of the column names corresponding to the predictor variable at each study wave if x_type = "longitudinal".
curveFun	A string specifying the functional form of the growth curve. Supported options include: "linear" (or "LIN"), and "bilinear spline" (or "BLS").
records	A list of numeric vectors, with each vector specifying the indices of the observed study waves for the corresponding longitudinal variable.
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 processes 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.

Value

An object of class myMxOutput. Depending on the paramOut argument, the object may contain the following slots:

• mxOutput: This slot contains the fitted longitudinal mediation 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.

References

• Liu, J., & Perera, R.A. (2022). Assessing Mediational Processes Using Piecewise Linear Growth Curve Models with Individual Measurement Occasions. Behavior Research Methods (Advance online publication). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-022-01940-2")}

• MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. Taylor & Francis Group/Lawrence Erlbaum Associates.

• Cheong, J., Mackinnon, D. P., & Khoo, S. T. (2003). Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling. Structural equation modeling: a multidisciplinary journal, 10(2), 238-262. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1207/S15328007SEM1002_5")}

• Soest, T., & Hagtvet, K. A. (2011). Mediation Analysis in a Latent Growth Curve Modeling Framework. Structural equation modeling: a multidisciplinary journal, 18(2), 289-314. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2011.557344")}

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$T1 - baseT
RMS_dat0$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$T9 - baseT
# Standardized time-invariant covariates
RMS_dat0$ex1 <- scale(RMS_dat0$Approach_to_Learning)


# Example 1: Baseline predictor, linear functional form
## Fit model
set.seed(20191029)
Med2_LGCM_LIN <- getMediation(
  dat = RMS_dat0, t_var = rep("T", 2), y_var = "M", m_var = "R", x_type = "baseline",
  x_var = "ex1", curveFun = "LIN", records = list(1:9, 1:9), res_scale = c(0.1, 0.1),
  res_cor = 0.3
  )

# Example 2: Longitudinal predictor, bilinear spline functional form
## Define parameter names
paraMed3_BLS <- c(
  "muetaX1", "muetaXr", "muetaX2", "mugX",
  paste0("psi", c("X1X1", "X1Xr", "X1X2", "XrXr", "XrX2", "X2X2")),
  "alphaM1", "alphaMr", "alphaM2", "mugM",
  paste0("psi", c("M1M1", "M1Mr", "M1M2", "MrMr", "MrM2", "M2M2"), "_r"),
  "alphaY1", "alphaYr", "alphaY2", "mugY",
  paste0("psi", c("Y1Y1", "Y1Yr", "Y1Y2", "YrYr", "YrY2", "Y2Y2"), "_r"),
  paste0("beta", c("X1Y1", "X1Yr", "X1Y2", "XrYr", "XrY2", "X2Y2",
                   "X1M1", "X1Mr", "X1M2", "XrMr", "XrM2", "X2M2",
                   "M1Y1", "M1Yr", "M1Y2", "MrYr", "MrY2", "M2Y2")),
  "muetaM1", "muetaMr", "muetaM2", "muetaY1", "muetaYr", "muetaY2",
  paste0("mediator", c("111", "11r", "112", "1rr", "1r2",
                       "122", "rr2", "r22", "rrr", "222")),
  paste0("total", c("11", "1r", "12", "rr", "r2", "22")),
  "residualsX", "residualsM", "residualsY", "residualsMX", "residualsYX", "residualsYM"
  )

## Fit model
set.seed(20191029)
Med3_LGCM_BLS <- getMediation(
  dat = RMS_dat0, t_var = rep("T", 3), y_var = "S", m_var = "M", x_type = "longitudinal",
  x_var = "R", curveFun = "bilinear spline", records = list(2:9, 1:9, 1:9),
  res_scale = c(0.1, 0.1, 0.1), res_cor = c(0.3, 0.3), tries = 10, paramOut = TRUE,
  names = paraMed3_BLS
  )
printTable(Med3_LGCM_BLS)

nlpsem documentation built on Sept. 13, 2023, 1:06 a.m.