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
##With direct effect of PS on RC
##definiiton of direct and indirect effects##
#direct <- mxAlgebra(c, name = "direct")
#indirect_Voc <- mxAlgebra(e*b, name = "indirect_Voc")
#indirect_WR <- mxAlgebra(i*a, name = "indirect_WR")
#indirect_PA_WR <- mxAlgebra(j*h*a, name = "indirect_PA_WR")
#indirect_MA_WR <- mxAlgebra(k*g*a, name = "indirect_MA_WR")
#indirect_PA_Voc <- mxAlgebra(j*d*b, name = "indirect_PA_Voc")
#indirect_MA_Voc <- mxAlgebra(k*f*a, name = "indirect_MA_Voc")

SEM13.13 <-
'RC ~ a*WR + b*Voc + c*PS
Voc ~ d*PA + e*PS +f*MA
WR ~ g*MA + h*PA + i*PS
PA ~ j*PS
MA ~ k*PS
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(e*b, name = "indirect_Voc")
indirect_WR <- mxAlgebra(i*a, name = "indirect_WR")
Indirect_PA_WR <- mxAlgebra(j*h*a, name = "indirect_PA_WR")
Indirect_MA_WR <- mxAlgebra(k*g*a, name = "indirect_MA_WR")
Indirect_PA_Voc <- mxAlgebra(j*d*b, name = "indirect_PA_Voc")
Indirect_MA_Voc <- mxAlgebra(k*f*a, 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
DF manually adjusted 0.0000
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