R: Fit a Longitudinal Mediation Model

getMediation {nlpsem}

R Documentation

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). 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. 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. 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)

[Package nlpsem version 0.3 Index]