# metaSEM_Indirect effects via multiple mediators

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Joined: 03/22/2022 - 10:52
metaSEM_Indirect effects via multiple mediators

Hello all,

I am intereseted to test the indirect effects with multiple sequential mediators. I am wondering how would it possible to estiamte it? I tried to use the R code below, but there are warnings listed below. It would be highly appreciated if anyone would provide your thoughts? Thank you so much in advance!

R.code

# indirect_MA_Voc <- mxAlgebra(kfa, name = "indirect_MA_Voc")

SEM13.13 <-
'RC ~ aWR + bVoc + cPS
Voc ~ d
PA + ePS +fMA
WR ~ gMA + hPA + iPS
PA ~ j
PS
MA ~ kPS
PS ~~ 1
PS
RC ~~ RC
Voc ~~ WR
PA ~~ MA'
plot(SEM13.13)

cormatrices13
TS1 <- tssem1(cormatrices13, n, method = "REM")
summary(TS1)

## Pooled correlation matrix

averageR <- vec2symMat(coef(TS1, select = "fixed"), diag = FALSE)

row_names <- varnames13
col_names <- varnames13
rownames(averageR) <- row_names
colnames(averageR) <- col_names

averageR

# Fit the pooled correlation matrix to the model

RAM13.13 <- lavaan2RAM(SEM13.13, obs.variables = varnames13)
RAM13.13

## definition of direct and indirect effects

direct <- mxAlgebra(c, name = "direct")
indirect_Voc <- mxAlgebra(eb, name = "indirect_Voc")
indirect_WR <- mxAlgebra(i
a, name = "indirect_WR")
Indirect_PA_WR <- mxAlgebra(jha, name = "indirect_PA_WR")
Indirect_MA_WR <- mxAlgebra(kga, name = "indirect_MA_WR")
Indirect_PA_Voc <- mxAlgebra(jdb, name = "indirect_PA_Voc")
Indirect_MA_Voc <- mxAlgebra(kfa, name = "indirect_MA_Voc")

TS2_13.13 <- tssem2(TS1, RAM = RAM13.13, diag.constraints = TRUE,
mx.algebras = list(Ind = mxAlgebra(e*b,name = "Ind")))

summary(TS2_13.13)

Results:

Estimate Std.Error lbound ubound
k 0.330281 NA 0.058848 0.477129
j 0.257587 NA 0.178920 0.393154
c 0.082494 NA -0.108852 0.322229
b 0.480529 NA 0.100640 0.595549
a 0.351818 NA 0.093652 0.672354
f 0.420111 NA -0.026672 NA
d 0.042787 NA -0.202462 0.380554
e 0.062226 NA NA 0.300401
g 0.227029 NA -0.032817 NA
h 0.267327 NA 0.014386 0.436680
i 0.185371 NA NA 0.317640
MAWITHMA 0.883892 NA 0.883892 0.891657
PAWITHMA 0.247157 NA 0.114809 0.513876
PAWITHPA 0.943245 NA 0.931907 0.943245
RCWITHRC 0.558048 NA 0.472119 NA
VocWITHVoc 0.698494 NA NA 0.816915
VocWITHWR 0.193708 NA -0.102425 0.281407
WRWITHWR 0.765924 NA NA NA
z value Pr(>|z|)
k NA NA
j NA NA
c NA NA
b NA NA
a NA NA
f NA NA
d NA NA
e NA NA
g NA NA
h NA NA
i NA NA
MAWITHMA NA NA
PAWITHMA NA NA
PAWITHPA NA NA
RCWITHRC NA NA
VocWITHVoc NA NA
VocWITHWR NA NA
WRWITHWR NA NA

mxAlgebras objects (and their 95% likelihood-based CIs):
lbound Estimate ubound
Ind[1,1] -0.05910647 0.02990153 0.1308758

Goodness-of-fit indices:
Value
Sample size 5511.0000
Chi-square of target model 45.4138
DF of target model 2.0000
p value of target model 0.0000
Number of constraints imposed on "Smatrix" 5.0000
Chi-square of independence model 1732.6199
DF of independence model 15.0000
RMSEA 0.0628
RMSEA lower 95% CI 0.0477
RMSEA upper 95% CI 0.0792
SRMR 0.0674
TLI 0.8104
CFI 0.9747
AIC 41.4138
BIC 28.1848
OpenMx status1: 3 ("0" or "1": The optimization is considered fine.
Other values indicate problems.)

Warning message:
In print.summary.wls(x) :
OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again.

It would be highly appreciated if you are wiling to help!!

Thank you so much!!

Best regards,

Eva

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Joined: 10/08/2009 - 22:37
I am afraid that I do not

I am afraid that I do not know what you want to achieve. Moreover, it is unclear what's wrong without a reproducible example.

The warning message is "OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again." Have you rerun the model to see if it improves?

Offline
Joined: 03/22/2022 - 10:52
Dear Mike,

Dear Mike,

Thank yo so much for your explaination! I am sorry that I did not put my question clear enough. I basically wanted to conduct the statistical test of the multiple indirect effects (multiple). More importantly, if we can examine both toal indirect effects and individual indirect effect?

I am wondering if I can put my code here again?

# Model specification

Model <-
'RC ~ aWR + bVoc + cMA
Voc ~ c
PA + dPS + eMA
WR ~ fMA + gPA + hPS
PA ~ i
PS
MA ~ jPS
PS ~~ 1
PS
RC ~~ RC
Indirect_wr : = ha
Indirect_voc : = d
b
Indirect_ma : = jc
Indirect_pa_voc : = i
cb
Indirect_pa_wr : = i
ga
Indirect_ma_voc : = j
eb
Indirect_ma_wr : = j
f*a'
plot(Model)

## Two-stage MASEM

cormatrices13
TS1 <- tssem1(cormatrices13, n, method = "REM")
summary(TS1)

## Pooled correlation matrix

averageR <- vec2symMat(coef(TS1, select = "fixed"), diag = FALSE)
averageR

## Fit the pooled correlation matrix to the model

RAM13.13 <- lavaan2RAM(SEM13.13, obs.variables = varnames13)
RAM13.13

TS2_13.13 <- tssem2(TS1, RAM = RAM13.13, intervals.type = "LB",
diag.constraints = FALSE,
mx.algebras = list(Ind = mxAlgebra(h*a,name = "Ind")))

summary(TS2_13.13)

TS2_13.13 <- tssem2(TS1, RAM = RAM13.13, intervals.type = "LB",
diag.constraints = FALSE,
mx.algebras = list(Ind = mxAlgebra(*a,name = "Ind")))

Thank you again for your help!

Best regards,
Eva

Offline
Joined: 10/08/2009 - 22:37
There are many syntactic

There are many syntactic errors. For example, the following R code is not a valid expression:

 mxAlgebra(*a, name = "Ind")

Do you mean?

 mxAlgebra(a*b, name = "Ind")

You may refer to the following examples.
https://github.com/mikewlcheung/code-in-articles/blob/master/Cheung%202021c/Supplementary2.md

Offline
Joined: 03/22/2022 - 10:52
Dear Mike,

Dear Mike,

Thank you so much for providing detailed instruction! Sorry to make you confused. I have corrected the typo(a*b). Thanks for pointing it out.

The reason I raise this question is because I am not quite sure whether it is ok to set the diag.constraints = FALSE. And the goodness of the fit was :

Chi-X2 (df = 5) = 115.55, p <.001, CFI = 0.94, NNFI = 0.81, RMSEA = 0.06, SRMR = 0.08.

I am wondering if this model is acceptable? In MASEM, if the criteria is stricter compared with SEM?

Thank you so much for your patience and valuable time!

Best regards,
Eva