MedStd {cTMed} | R Documentation |
Standardized Total, Direct, and Indirect Effects of X on Y Through M Over a Specific Time Interval or a Range of Time Intervals
Description
This function computes the standardized total, direct, and indirect effects
of the independent variable X
on the dependent variable Y
through mediator variables \mathbf{m}
over a specific time interval \Delta t
or a range of time intervals
using the first-order stochastic differential equation model's
drift matrix \boldsymbol{\Phi}
and process noise covariance matrix \boldsymbol{\Sigma}
.
Usage
MedStd(phi, sigma, delta_t, from, to, med, tol = 0.01)
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 |
tol |
Numeric. Smallest possible time interval to allow. |
Details
See TotalStd()
,
DirectStd()
, and
IndirectStd()
for more details.
Value
Returns an object
of class ctmedmed
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- fun
Function used ("MedStd").
- output
A matrix of total, direct, and indirect effects.
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:
DeltaBeta()
,
DeltaBetaStd()
,
DeltaIndirectCentral()
,
DeltaMed()
,
DeltaMedStd()
,
DeltaTotalCentral()
,
Direct()
,
DirectStd()
,
ExpCov()
,
ExpMean()
,
Indirect()
,
IndirectCentral()
,
IndirectStd()
,
MCBeta()
,
MCBetaStd()
,
MCIndirectCentral()
,
MCMed()
,
MCMedStd()
,
MCPhi()
,
MCTotalCentral()
,
Med()
,
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
)
# Specific time interval ----------------------------------------------------
MedStd(
phi = phi,
sigma = sigma,
delta_t = 1,
from = "x",
to = "y",
med = "m"
)
# Range of time intervals ---------------------------------------------------
med <- MedStd(
phi = phi,
sigma = sigma,
delta_t = 1:30,
from = "x",
to = "y",
med = "m"
)
plot(med)
# Methods -------------------------------------------------------------------
# MedStd has a number of methods including
# print, summary, and plot
med <- MedStd(
phi = phi,
sigma = sigma,
delta_t = 1:5,
from = "x",
to = "y",
med = "m"
)
print(med)
summary(med)
plot(med)