[R-meta] Constraint rrror when using Wald_test_cwb

[Pearl, Brendan]

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)
```