IndirectStd: Standardized Indirect Effect of X on Y Through M Over a...

In cTMed: Continuous-Time Mediation

Standardized Indirect Effect of X on Y Through M Over a Specific Time Interval

Description

This function computes the standardized indirect effect of the independent variable X on the dependent variable Y through mediator variables \mathbf{m} over a specific time interval \Delta t using the first-order stochastic differential equation model's drift matrix \boldsymbol{\Phi} and process noise covariance matrix \boldsymbol{\Sigma}.

Usage

IndirectStd(phi, sigma, delta_t, from, to, med)

Arguments

phi	Numeric matrix. The drift matrix (\boldsymbol{\Phi}). phi should have row and column names pertaining to the variables in the system.
sigma	Numeric matrix. The process noise covariance matrix (\boldsymbol{\Sigma}).
delta_t	Numeric. Time interval (\Delta t).
from	Character string. Name of the independent variable X in phi.
to	Character string. Name of the dependent variable Y in phi.
med	Character vector. Name/s of the mediator variable/s in phi.

Details

The standardized indirect effect of the independent variable X on the dependent variable Y relative to some mediator variables \mathbf{m} over a specific time interval \Delta t is given by

\mathrm{Indirect}^{\ast}_{{\Delta t}_{i, j}} = \mathrm{Total}^{\ast}_{{\Delta t}_{i, j}} - \mathrm{Direct}^{\ast}_{{\Delta t}_{i, j}}

where \mathrm{Total}^{\ast}_{\Delta t} and \mathrm{Direct}^{\ast}_{\Delta t} are standardized total and direct effects for time interval \Delta t.

Value

Returns an object of class ctmedeffect which is a list with the following elements:

call

Function call.

args

Function arguments.

fun

Function used ("IndirectStd").

output

The standardized indirect effect.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. Sociological Methodology, 17, 37. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/271028")}

Deboeck, P. R., & Preacher, K. J. (2015). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling: A Multidisciplinary Journal, 23 (1), 61–75. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2014.973960")}

Ryan, O., & Hamaker, E. L. (2021). Time to intervene: A continuous-time approach to network analysis and centrality. Psychometrika, 87 (1), 214–252. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11336-021-09767-0")}

See Also

Other Continuous-Time Mediation Functions: BootBeta(), BootBetaStd(), BootIndirectCentral(), BootMed(), BootMedStd(), BootTotalCentral(), DeltaBeta(), DeltaBetaStd(), DeltaIndirectCentral(), DeltaMed(), DeltaMedStd(), DeltaTotalCentral(), Direct(), DirectStd(), Indirect(), IndirectCentral(), MCBeta(), MCBetaStd(), MCIndirectCentral(), MCMed(), MCMedStd(), MCPhi(), MCPhiSigma(), MCTotalCentral(), Med(), MedStd(), PosteriorBeta(), PosteriorIndirectCentral(), PosteriorMed(), PosteriorTotalCentral(), Total(), TotalCentral(), TotalStd(), Trajectory()

Examples

phi <- matrix(
  data = c(
    -0.357, 0.771, -0.450,
    0.0, -0.511, 0.729,
    0, 0, -0.693
  ),
  nrow = 3
)
colnames(phi) <- rownames(phi) <- c("x", "m", "y")
sigma <- matrix(
  data = c(
    0.24455556, 0.02201587, -0.05004762,
    0.02201587, 0.07067800, 0.01539456,
    -0.05004762, 0.01539456, 0.07553061
  ),
  nrow = 3
)
delta_t <- 1
IndirectStd(
  phi = phi,
  sigma = sigma,
  delta_t = delta_t,
  from = "x",
  to = "y",
  med = "m"
)

cTMed documentation built on Nov. 5, 2025, 7:18 p.m.