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Hi Mike,
Hope you're well. I was just wondering whether there's a simple way to bootstrap the indirect effect in metasem (tssem2 or osmasem).
Thanks,
Best wishes,
Pasco
Hello All,
In my reproducible example below, you'll see that
Cov_R(sampling covariances for the R matrix) is positive definite.However, why when I run the following I get an error message saying?:
metaSEM::wls(R, aCov = Cov_R, RAM = RAM3, n = 9201)
> [1] Error: "Cov" is not positive definite.
Thanks,
Simon
--- Full Reproducible R code:
R <- structure(c(1, 0.5846, 0.5476, 0.4127, 0.6029, 0.5104, 0.4724,
0.4289, 0.5846, 1, 0.2701, 0.4449, 0.3114, 0.3607, 0.5946, 0.605,
0.5476, 0.2701, 1, 0.4459, 0.5791, 0.4243, 0.4671, 0.5854, 0.4127,
0.4449, 0.4459, 1, 0.4677, 0.5889, 0.6848, 0.5456, 0.6029, 0.3114,
0.5791, 0.4677, 1, 0.466, 0.578, 0.607, 0.5104, 0.3607, 0.4243,
0.5889, 0.466, 1, 0.6488, 0.5677, 0.4724, 0.5946, 0.4671, 0.6848,
0.578, 0.6488, 1, 0.3737, 0.4289, 0.605, 0.5854, 0.5456, 0.607,
0.5677, 0.3737, 1), dim = c(8L, 8L), dimnames = list(c("L2R",
"L2DA", "L2DF", "L2V", "L2G", "L2P", "L2M", "L2L"), c("L2R", "L2DA", "L2DF", "L2V", "L2G", "L2P", "L2M", "L2L")))
Cov_R <- structure(c(0.0149, 0.0087, 0.0092, 0.0086, 0.0088, 0.0089, 0.0089,
0.0094, 0.009, 0.0094, 0.0093, 0.0088, 0.0087, 0.0087, 0.0088,
0.0096, 0.0085, 0.0089, 0.0093, 0.0085, 0.009, 0.0087, 0.0087,
0.0086, 0.0085, 0.009, 0.0087, 0.0087, 0.0087, 0.018, 0.0086,
0.0087, 0.0087, 0.0089, 0.0087, 0.0087, 0.0087, 0.0087, 0.0087,
0.0088, 0.0092, 0.0095, 0.0087, 0.0087, 0.0085, 0.0088, 0.0087,
0.0088, 0.0087, 0.0087, 0.0087, 0.0086, 0.0088, 0.0087, 0.0087,
0.0087, 0.0092, 0.0086, 0.0143, 0.0087, 0.0086, 0.0088, 0.0088,
0.0093, 0.0088, 0.009, 0.009, 0.0088, 0.0087, 0.0086, 0.0092,
0.0095, 0.0084, 0.0089, 0.0091, 0.0085, 0.0092, 0.0087, 0.0086,
0.0086, 0.0085, 0.009, 0.0087, 0.0085, 0.0086, 0.0087, 0.0087,
0.0132, 0.0087, 0.0087, 0.0086, 0.0086, 0.0088, 0.0087, 0.0086,
0.0085, 0.0089, 0.0087, 0.0089, 0.0086, 0.0088, 0.0087, 0.0086,
0.0084, 0.0086, 0.0089, 0.0086, 0.0084, 0.0084, 0.0086, 0.0088,
0.0087, 0.0088, 0.0087, 0.0086, 0.0087, 0.0122, 0.0088, 0.0086,
0.0087, 0.0087, 0.009, 0.0087, 0.0085, 0.0087, 0.0089, 0.0086,
0.009, 0.0087, 0.0088, 0.0087, 0.0084, 0.0086, 0.0087, 0.0089,
0.0084, 0.0084, 0.0085, 0.0086, 0.0089, 0.0089, 0.0089, 0.0088,
0.0087, 0.0088, 0.0108, 0.0087, 0.0088, 0.0087, 0.0088, 0.0088,
0.0086, 0.0088, 0.0088, 0.0088, 0.0087, 0.0086, 0.0088, 0.0088,
0.0085, 0.0087, 0.0087, 0.0087, 0.0085, 0.0085, 0.0087, 0.0087,
0.0088, 0.0089, 0.0087, 0.0088, 0.0086, 0.0086, 0.0087, 0.0243,
0.0091, 0.0093, 0.0088, 0.0093, 0.0092, 0.0092, 0.0087, 0.0092,
0.0088, 0.0084, 0.0087, 0.0095, 0.009, 0.009, 0.0087, 0.0086,
0.0088, 0.0089, 0.0091, 0.0088, 0.0085, 0.0094, 0.0087, 0.0093,
0.0086, 0.0087, 0.0088, 0.0091, 0.0181, 0.0091, 0.0092, 0.0093,
0.009, 0.009, 0.0087, 0.0091, 0.0099, 0.0084, 0.0089, 0.0092,
0.0086, 0.0092, 0.0087, 0.0087, 0.0087, 0.0086, 0.0092, 0.0087,
0.0086, 0.009, 0.0087, 0.0088, 0.0088, 0.0087, 0.0087, 0.0093,
0.0091, 0.0238, 0.0092, 0.0094, 0.009, 0.0092, 0.0087, 0.0093,
0.0089, 0.0087, 0.0088, 0.0092, 0.0087, 0.0089, 0.0091, 0.0087,
0.0087, 0.0087, 0.009, 0.0091, 0.0087, 0.0094, 0.0087, 0.009,
0.0087, 0.009, 0.0088, 0.0088, 0.0092, 0.0092, 0.0237, 0.0092,
0.0087, 0.0087, 0.0087, 0.0088, 0.0105, 0.0087, 0.0089, 0.0092,
0.0085, 0.0088, 0.0087, 0.0089, 0.0086, 0.0085, 0.0088, 0.0087,
0.0089, 0.0093, 0.0087, 0.009, 0.0086, 0.0087, 0.0088, 0.0093,
0.0093, 0.0094, 0.0092, 0.014, 0.0089, 0.0089, 0.0087, 0.009,
0.009, 0.0085, 0.0088, 0.0093, 0.0087, 0.009, 0.0088, 0.0087,
0.0087, 0.0086, 0.009, 0.0088, 0.0087, 0.0088, 0.0088, 0.0088,
0.0085, 0.0085, 0.0086, 0.0092, 0.009, 0.009, 0.0087, 0.0089,
0.0163, 0.009, 0.0087, 0.0091, 0.0087, 0.0083, 0.0087, 0.0089,
0.0092, 0.0094, 0.0087, 0.0085, 0.0088, 0.0091, 0.0096, 0.0086,
0.0084, 0.0087, 0.0092, 0.0087, 0.0089, 0.0087, 0.0088, 0.0092,
0.009, 0.0092, 0.0087, 0.0089, 0.009, 0.0237, 0.0092, 0.0092,
0.0087, 0.0086, 0.0088, 0.0088, 0.0088, 0.0088, 0.009, 0.0086,
0.0086, 0.0088, 0.0089, 0.009, 0.0087, 0.0087, 0.0095, 0.0086,
0.0087, 0.0089, 0.0088, 0.0087, 0.0087, 0.0087, 0.0087, 0.0087,
0.0087, 0.0092, 0.0309, 0.0087, 0.0087, 0.0087, 0.0088, 0.0087,
0.0087, 0.0086, 0.0087, 0.0089, 0.0086, 0.0087, 0.0087, 0.0087,
0.009, 0.0088, 0.0087, 0.0092, 0.0089, 0.0086, 0.0088, 0.0092,
0.0091, 0.0093, 0.0088, 0.009, 0.0091, 0.0092, 0.0087, 0.0236,
0.0088, 0.0085, 0.0088, 0.0089, 0.0087, 0.0091, 0.009, 0.0086,
0.0087, 0.0087, 0.0092, 0.0091, 0.0086, 0.0096, 0.0087, 0.0095,
0.0086, 0.009, 0.0087, 0.0088, 0.0099, 0.0089, 0.0105, 0.009,
0.0087, 0.0087, 0.0087, 0.0088, 0.0532, 0.0085, 0.009, 0.0093,
0.0085, 0.0091, 0.0086, 0.009, 0.0085, 0.0085, 0.0088, 0.0086,
0.0086, 0.0085, 0.0085, 0.0084, 0.0088, 0.0087, 0.0086, 0.0084,
0.0084, 0.0087, 0.0087, 0.0085, 0.0083, 0.0086, 0.0087, 0.0085,
0.0085, 0.0161, 0.0086, 0.0085, 0.0082, 0.0084, 0.0087, 0.0087,
0.0083, 0.0082, 0.0084, 0.0087, 0.0087, 0.0089, 0.0088, 0.0089,
0.0087, 0.0088, 0.0088, 0.0087, 0.0089, 0.0088, 0.0089, 0.0088,
0.0087, 0.0088, 0.0088, 0.0088, 0.009, 0.0086, 0.0104, 0.0088,
0.0085, 0.0088, 0.0087, 0.0087, 0.0085, 0.0085, 0.0088, 0.0087,
0.0087, 0.0093, 0.0087, 0.0091, 0.0086, 0.0087, 0.0088, 0.0095,
0.0092, 0.0092, 0.0092, 0.0093, 0.0089, 0.0088, 0.0087, 0.0089,
0.0093, 0.0085, 0.0088, 0.0122, 0.0086, 0.0089, 0.0087, 0.0087,
0.0087, 0.0086, 0.0089, 0.0087, 0.0086, 0.0085, 0.0088, 0.0085,
0.0084, 0.0084, 0.0085, 0.009, 0.0086, 0.0087, 0.0085, 0.0087,
0.0092, 0.0088, 0.0087, 0.0087, 0.0085, 0.0082, 0.0085, 0.0086,
0.011, 0.0088, 0.0085, 0.0084, 0.0086, 0.009, 0.0089, 0.0084,
0.0083, 0.009, 0.0087, 0.0092, 0.0086, 0.0086, 0.0087, 0.009,
0.0092, 0.0089, 0.0088, 0.009, 0.0094, 0.0088, 0.0086, 0.0091,
0.0091, 0.0084, 0.0088, 0.0089, 0.0088, 0.0118, 0.0087, 0.0086,
0.0087, 0.0087, 0.0093, 0.0087, 0.0085, 0.0087, 0.0087, 0.0087,
0.0089, 0.0087, 0.0087, 0.0087, 0.0087, 0.0091, 0.0087, 0.0088,
0.0087, 0.009, 0.0087, 0.009, 0.0086, 0.0087, 0.0087, 0.0087,
0.0085, 0.0087, 0.0124, 0.0086, 0.0085, 0.0085, 0.0087, 0.0089,
0.0086, 0.0087, 0.0087, 0.0086, 0.0086, 0.0089, 0.0087, 0.0086,
0.0087, 0.0087, 0.0089, 0.0087, 0.0085, 0.0086, 0.0089, 0.0086,
0.009, 0.0087, 0.0087, 0.0087, 0.0084, 0.0086, 0.0086, 0.0116,
0.0085, 0.0084, 0.0086, 0.0086, 0.0088, 0.0086, 0.0086, 0.0086,
0.0084, 0.0084, 0.0085, 0.0088, 0.0087, 0.0087, 0.0086, 0.0087,
0.0088, 0.0086, 0.0086, 0.0087, 0.0085, 0.0083, 0.0085, 0.0087,
0.0086, 0.0087, 0.0085, 0.0085, 0.0095, 0.0086, 0.0088, 0.0085,
0.0084, 0.0085, 0.0088, 0.0085, 0.0084, 0.0084, 0.0085, 0.0089,
0.0086, 0.0087, 0.0085, 0.0086, 0.0091, 0.0088, 0.0087, 0.0087,
0.0085, 0.0082, 0.0085, 0.0086, 0.009, 0.0087, 0.0085, 0.0084,
0.0086, 0.011, 0.0089, 0.0084, 0.0083, 0.009, 0.0087, 0.009,
0.0086, 0.0085, 0.0087, 0.0091, 0.0092, 0.009, 0.0088, 0.009,
0.0096, 0.0089, 0.0087, 0.0092, 0.0088, 0.0084, 0.0088, 0.0089,
0.0089, 0.0093, 0.0087, 0.0086, 0.0088, 0.0089, 0.0127, 0.0087,
0.0085, 0.0087, 0.0087, 0.0087, 0.0088, 0.0086, 0.0087, 0.0088,
0.0087, 0.0091, 0.0087, 0.0088, 0.0086, 0.009, 0.0087, 0.0091,
0.0086, 0.0087, 0.0087, 0.0087, 0.0084, 0.0087, 0.0089, 0.0086,
0.0085, 0.0084, 0.0087, 0.013, 0.0086, 0.0087, 0.0087, 0.0085,
0.0087, 0.0089, 0.0088, 0.0085, 0.0086, 0.0087, 0.0089, 0.0087,
0.0084, 0.0087, 0.009, 0.0086, 0.0086, 0.0087, 0.0087, 0.0086,
0.0083, 0.0085, 0.0086, 0.0088, 0.0084, 0.0083, 0.0085, 0.0086,
0.0131), dim = c(28L, 28L))
library(metaSEM)
Positive definite checks OK:
is.pd(Cov_R, cor.analysis=FALSE)
> [1] TRUE
ModelC <-
'Comprehension =~ L2L + L2V + L2G
Decoding =~ L2DA + L2DF
Meta =~ L2P + L2M
L2R ~ Comprehension + Decoding
Comprehension ~~ 0 * Meta
Decoding ~ 0 * Meta
Comprehension ~~ Decoding'
RAM3 <- lavaan2RAM(ModelC, obs.variables = varnames,
A.notation = "on", S.notation = "with")
wls(R, aCov = Cov_R, RAM = RAM3, n = 9201)
[1] > "Cov" is not positive definite.
Dear Simon,
The problem is due to the R, not Cov_R.
> is.pd(R)
[1] FALSE
By the way, please use a new subject next time, as unrelated follow-ups may confuse readers.
Mike
Dear Mike,
Many thanks for your response. Do you have any suggestion to resolve the problem?
I can think of using nearest PD of R like: is.pd(Matrix::as.matrix(nearPD(R, corr = TRUE)$mat))
There is an issue with your data. I recommend investigating the cause, such as whether it is due to using pairwise deletion.
While adjusting the data with near PD may help, the more critical question is whether the results obtained from this modified data can be considered trustworthy.
Thanks, Mike. Do you mean investigating whether the primary studies had used pairwise deletion when calculating their correlations OR investigating whether I, as the meta-analyst, have used some kind of deletion, such as na.omit(data), when preparing my data for meta-analysis??
In my case, several of the primary studies have NOT reported on correlations between some pairs of variables. Therefore, the data prepared for meta-analysis contains many NAs for those pairs of variables which I deleted using listwise deletion.
It is the latter, as it is a meta-analysis.
Thank you, Mike. As I mentioned, several of the primary studies have NOT reported on correlations between some pairs of variables. Therefore, the data prepared for meta-analysis contains many NAs for those pairs of variables which I deleted using listwise deletion i.e., na.omit(data).
Are you suggesting that I don't remove the rows where r values are missing?
There is no need to remove studies with NA. The tssem1() function uses FIML in OpneMx to handle missing data.
Please refer to the relevant literature and examples.
Thank you, Mike. I'm not using tssem1() for the first stage of meta-analysis. Instead, I'm using metafor::rma.mv().