PosteriorIndirectCentral {cTMed} | R Documentation |
Posterior Distribution of the Indirect Effect Centrality Over a Specific Time Interval or a Range of Time Intervals
Description
This function generates a posterior
distribution
of the indirect effect centrality
over a specific time interval \Delta t
or a range of time intervals
using the posterior distribution
of the first-order stochastic differential equation model
drift matrix \boldsymbol{\Phi}
.
Usage
PosteriorIndirectCentral(phi, delta_t, ncores = NULL)
Arguments
phi |
List of numeric matrices.
Each element of the list is a sample
from the posterior distribution
of the drift matrix ( |
delta_t |
Numeric.
Time interval
( |
ncores |
Positive integer.
Number of cores to use.
If |
Details
See TotalCentral()
for more details.
Value
Returns an object
of class ctmedmc
which is a list with the following elements:
- call
Function call.
- args
Function arguments.
- fun
Function used ("PosteriorIndirectCentral").
- output
A list the length of which is equal to the length of
delta_t
.
Each element in the output
list has the following elements:
- est
Mean of the posterior distribution of the total, direct, and indirect effects.
- thetahatstar
Posterior distribution of the 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()
,
DeltaIndirectCentral()
,
DeltaMed()
,
DeltaTotalCentral()
,
Direct()
,
Indirect()
,
IndirectCentral()
,
MCBeta()
,
MCIndirectCentral()
,
MCMed()
,
MCPhi()
,
MCTotalCentral()
,
Med()
,
PosteriorBeta()
,
PosteriorMed()
,
PosteriorPhi()
,
PosteriorTotalCentral()
,
Total()
,
TotalCentral()
,
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")
vcov_phi_vec <- matrix(
data = c(
0.002704274, -0.001475275, 0.000949122,
-0.001619422, 0.000885122, -0.000569404,
0.00085493, -0.000465824, 0.000297815,
-0.001475275, 0.004428442, -0.002642303,
0.000980573, -0.00271817, 0.001618805,
-0.000586921, 0.001478421, -0.000871547,
0.000949122, -0.002642303, 0.006402668,
-0.000697798, 0.001813471, -0.004043138,
0.000463086, -0.001120949, 0.002271711,
-0.001619422, 0.000980573, -0.000697798,
0.002079286, -0.001152501, 0.000753,
-0.001528701, 0.000820587, -0.000517524,
0.000885122, -0.00271817, 0.001813471,
-0.001152501, 0.00342605, -0.002075005,
0.000899165, -0.002532849, 0.001475579,
-0.000569404, 0.001618805, -0.004043138,
0.000753, -0.002075005, 0.004984032,
-0.000622255, 0.001634917, -0.003705661,
0.00085493, -0.000586921, 0.000463086,
-0.001528701, 0.000899165, -0.000622255,
0.002060076, -0.001096684, 0.000686386,
-0.000465824, 0.001478421, -0.001120949,
0.000820587, -0.002532849, 0.001634917,
-0.001096684, 0.003328692, -0.001926088,
0.000297815, -0.000871547, 0.002271711,
-0.000517524, 0.001475579, -0.003705661,
0.000686386, -0.001926088, 0.004726235
),
nrow = 9
)
phi <- MCPhi(
phi = phi,
vcov_phi_vec = vcov_phi_vec,
R = 1000L
)$output
# Specific time interval ----------------------------------------------------
PosteriorIndirectCentral(
phi = phi,
delta_t = 1
)
# Range of time intervals ---------------------------------------------------
posterior <- PosteriorIndirectCentral(
phi = phi,
delta_t = 1:5
)
# Methods -------------------------------------------------------------------
# PosteriorIndirectCentral has a number of methods including
# print, summary, confint, and plot
print(posterior)
summary(posterior)
confint(posterior, level = 0.95)
plot(posterior)