DirectStd {cTMed} | R Documentation |
Standardized Direct Effect of X on Y Over a Specific Time Interval
Description
This function computes the standardized direct 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
DirectStd(phi, sigma, delta_t, from, to, med)
Arguments
phi |
Numeric matrix.
The drift matrix ( |
sigma |
Numeric matrix.
The process noise covariance matrix ( |
delta_t |
Numeric.
Time interval
( |
from |
Character string.
Name of the independent variable |
to |
Character string.
Name of the dependent variable |
med |
Character vector.
Name/s of the mediator variable/s in |
Details
The standardized direct effect
of the independent variable X
on the dependent variable Y
relative to some mediator variables \mathbf{m}
is given by
\mathrm{Direct}^{\ast}_{{\Delta t}_{i, j}}
=
\mathrm{Direct}_{{\Delta t}_{i, j}}
\left(
\frac{\sigma_{{x}_{j}}}{\sigma_{{y}_{i}}}
\right)
where
\boldsymbol{\Phi}
denotes the drift matrix,
\sigma_{{x}_{j}}
and \sigma_{{y}_{i}}
are the steady-state model-implied standard deviations
of the state independent and dependent variables, respectively, and
\Delta t
the time interval.
Value
Returns an object
of class ctmedeffect
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- fun
Function used ("DirectStd").
- output
The standardized direct 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. 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. 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. 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()
,
Indirect()
,
IndirectCentral()
,
IndirectStd()
,
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
DirectStd(
phi = phi,
sigma = sigma,
delta_t = delta_t,
from = "x",
to = "y",
med = "m"
)