[R-meta] Constraint rrror when using Wald_test_cwb

[James Pustejovsky]

Wald_test_cwb() is for testing null hypotheses specified by a constraint or constraints on the model coefficients. In your MWE, you fit a summary meta-analysis with only one beta coefficient, so the constraints can only refer to that first coefficient (hence “Constraint indices must be less than or equal to 1”).

As an aside, note that the same error would occur if you use the more basic Wald_test() from clubSandwich.

James

> On Sep 14, 2024, at 7:32 AM, Michael Dewey via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org> wrote:
> 
> Dear Brendan
> 
> When I run your MWE (after inserting library(metafor) I get
> 
> Error in Wald_test_cwb(full_model = meta_analysis_robust, constraints = constrain_equal(1:3),  :
>  could not find function "Wald_test_cwb"
> 
> Michael
> 
> 
>> On 14/09/2024 10:28, Pearl, Brendan via R-sig-meta-analysis wrote:
>> Hello,
>> I am trying to run a cluster wild bootstrap, but am getting the following error:
>> Error in constrain_zero(constraints = constraints, coefs = coefs) :
>>   Constraint indices must be less than or equal to 1.
>>   Question: What does this mean?
>> Thankyou,
>> Brendan
>> Background (if relevant):
>> I am running a purely exploratory series of meta-analyses of the relationships between several predictors and outcomes (i.e. n x m meta-analyses).
>> There is non-independence within each predictor-relationship pair (some studies report multiple effect sizes for the same group of participants) and the effect sizes are nested.
>> I am following the general workflow outlined here: (https://wviechtb.github.io/metafor/reference/misc-recs.html#general-workflow-for-meta-analyses-involving-complex-dependency-structures) and want to use cluster wild bootstrapping because some analyses have very few studies (and cluster-robust inference methods led to very wide confidence intervals)
>> Minimal working example:
>> ```{r}
>> dat_temp_mwe <- structure(list(study = c("A", "B", "C", "D", "E", "E", "F", "F",
>>     "F", "F", "G"), effect_id = c(11, 28, 73, 93, 115, 232, 236,
>>     242, 252, 266, 284), Paper = c("AA", "BB", "CC", "DD", "EE1",
>>     "EE2", "FF1", "FF2", "FF3", "FF4", "GG"), Mean_age_when_outcome_measured = c(21,
>>     19, 26, 19, 19, 21, 21, 21, 21, 19, 19), yi = structure(c(-0.0401817896328312,
>>     -0.0700000000000002, -0.151002873536528, -0.113328685307003,
>>     -0.139761942375159, -0.0392207131532808, -0.0487901641694324,
>>     -0.05, -0.041141943331175, -0.0421011760186351, -0.604315966853329
>>     ), ni = c(1566, 844, 624, 355, 7449, 2135, 2410, 4853, 6912,
>>     7842, 1202), measure = "GEN"), vi = c(0.00014647659162424, 0.000527143487544687,
>>     0.00336452354486442, 0.00116040765035603, 0.00667694039383453,
>>     9.6295107522168e-05, 9.44692075770055e-05, 0.000100003675148229,
>>     2.50009187870589e-05, 2.50009187870581e-05, 0.0292124283937479
>>     )), row.names = c(NA, 11L), class = c("escalc", "data.frame"), yi.names = "yi", vi.names = "vi", digits = c(est = 4,
>>     se = 4, test = 4, pval = 4, ci = 4, var = 4, sevar = 4, fit = 4,
>>     het = 4))
>>     V <- vcalc(vi,
>>     cluster = study,
>>     obs = effect_id,
>>     time1 = Mean_age_when_outcome_measured,
>>     data = dat_temp_mwe,
>>     rho = 0.8,
>>     phi = 0.9)
>>   meta_analysis_output <- rma.mv(
>>     yi,
>>     V = V,
>>     random = ~ 1 | study / Paper / effect_id,
>>     data = dat_temp_mwe,
>>     control = list(rel.tol = 1e-8))
>>   Wald_test_cwb(full_model = meta_analysis_robust,
>>     constraints = constrain_equal(1:3),
>>     R = 99,
>>     seed = 20201229)
>> ```
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> 
> --
> Michael
> 
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