Support _metafor_ package (predictions and plots)

[arthur-albuquerque]

Hi,

It seems that model_parameters() isn't calculating the correct point estimate (and 95% CI) for all subgroups in a metafor::rma() object. Below, I show that predict() and emmeans() yield the same results for all subgroups, while model_parameters() only recovers the "alternate" subgroup correctly.

# remotes::install_github("wviechtb/metafor") # install version 3.9-5
library(metafor)
library(emmeans)
library(parameters)

dat <- metadat::dat.bcg 

datRR <- metafor::escalc(measure="RR",
                         data=dat,
                         ai=tpos, bi=tneg, ci=cpos, di=cneg)

res <- metafor::rma(data=datRR,
                         method = "REML",
                         mods = ~ alloc,
                         yi = yi, vi = vi,
                         slab = author)

predict(res, newmods = rbind(c(1,0), # Random effects
                             c(0,0), # alternate
                             c(0,1)), # systematic
        digits=3) 
#> 
#>     pred    se  ci.lb  ci.ub  pi.lb pi.ub 
#> 1 -0.966 0.267 -1.490 -0.442 -2.255 0.324 
#> 2 -0.518 0.441 -1.383  0.347 -1.980 0.944 
#> 3 -0.429 0.345 -1.105  0.247 -1.788 0.930

gr <- metafor::emmprep(res) # version 3.9-5 required

emmeans::emmeans(gr, specs = ~alloc) 
#>  alloc      emmean    SE  df asymp.LCL asymp.UCL
#>  alternate  -0.518 0.441 Inf     -1.38     0.347
#>  random     -0.966 0.267 Inf     -1.49    -0.442
#>  systematic -0.429 0.345 Inf     -1.10     0.247
#> 
#> Results are given on the log (not the response) scale. 
#> Confidence level used: 0.95

parameters::model_parameters(res)
#> # Random Effects
#> 
#> Parameter         | Coefficient |   SE |         95% CI |      z |      p | Weight
#> ----------------------------------------------------------------------------------
#> Aronson           |       -0.89 | 0.57 | [-2.01,  0.23] |  -1.56 | 0.119  |   3.07
#> Ferguson & Simes  |       -1.59 | 0.44 | [-2.45, -0.72] |  -3.59 | < .001 |   5.14
#> Rosenthal et al.1 |       -1.35 | 0.64 | [-2.61, -0.08] |  -2.09 | 0.036  |   2.41
#> Hart & Sutherland |       -1.44 | 0.14 | [-1.72, -1.16] | -10.19 | < .001 |  49.97
#> Vandiviere et al  |       -1.62 | 0.47 | [-2.55, -0.70] |  -3.43 | < .001 |   4.48
#> TPT Madras        |        0.01 | 0.06 | [-0.11,  0.14] |   0.19 | 0.849  | 252.42
#> Coetzee & Berjak  |       -0.47 | 0.24 | [-0.94,  0.00] |  -1.98 | 0.048  |  17.72
#> Overall           |       -0.45 | 0.52 | [-1.46,  0.56] |  -0.87 | 0.385  |       
#> 
#> # alternate
#> 
#> Parameter            | Coefficient |   SE |         95% CI |     z |      p | Weight
#> ------------------------------------------------------------------------------------
#> Frimodt-Moller et al |       -0.22 | 0.23 | [-0.66,  0.23] | -0.96 | 0.336  |  19.53
#> Stein & Aronson      |       -0.79 | 0.08 | [-0.95, -0.62] | -9.46 | < .001 | 144.81
#> Overall              |       -0.52 | 0.44 | [-1.38,  0.35] | -1.17 | 0.240  |       
#> 
#> # systematic
#> 
#> Parameter          | Coefficient |   SE |         95% CI |     z |      p | Weight
#> ----------------------------------------------------------------------------------
#> Rosenthal et al.2  |       -1.37 | 0.27 | [-1.90, -0.84] | -5.07 | < .001 |  13.69
#> Comstock et al.1   |       -0.34 | 0.11 | [-0.56, -0.12] | -3.05 | 0.002  |  80.57
#> Comstock & Webster |        0.45 | 0.73 | [-0.98,  1.88] |  0.61 | 0.541  |   1.88
#> Comstock et al.2   |       -0.02 | 0.27 | [-0.54,  0.51] | -0.06 | 0.948  |  14.00
#> Overall            |        0.09 | 0.56 | [-1.01,  1.19] |  0.16 | 0.874  |
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Catalina 10.15.7
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] parameters_0.18.2 emmeans_1.8.0     metafor_3.9-5     metadat_1.2-0    
#> [5] Matrix_1.4-1     
#> 
#> loaded via a namespace (and not attached):
#>  [1] rstudioapi_0.14    knitr_1.40         mathjaxr_1.6-0     magrittr_2.0.3    
#>  [5] insight_0.18.2     xtable_1.8-4       lattice_0.20-45    rlang_1.0.4       
#>  [9] fastmap_1.1.0      stringr_1.4.1      tools_4.2.1        grid_4.2.1        
#> [13] nlme_3.1-157       xfun_0.32          coda_0.19-4        cli_3.3.0         
#> [17] bayestestR_0.12.1  withr_2.5.0        htmltools_0.5.3    datawizard_0.5.1  
#> [21] yaml_2.3.5         digest_0.6.29      lifecycle_1.0.1    fs_1.5.2          
#> [25] glue_1.6.2         evaluate_0.16      rmarkdown_2.16     reprex_2.0.2      
#> [29] stringi_1.7.8      estimability_1.4.1 compiler_4.2.1     mvtnorm_1.1-3

^{Created on 2022-09-03 with reprex v2.0.2}

[arthur-albuquerque]

metafor is an excellent package, but there isn't a built-in easy way to plot results from subgroups.

By contrast, the R package meta has a nice forest() function that quickly plots by subgroup:

I think parameters/see could fill this gap in metafor's functionalities by showing model predictions instead of summary results.

What do you think?

[strengejacke]

I'd say we should have both. But this would indeed be something for modelbased.

[strengejacke]

@bwiernik @DominiqueMakowski just to let you know. I don't know how to implement a predict-method for rma, there's also a years-old issue for ggeffects: strengejacke/ggeffects#30

[arthur-albuquerque]

Perhaps the new function metafor::emmprep can help. Its features were described by @wviechtb in this thread.

[bwiernik]

I have work in progress on improving support for metafor in easystats: easystats/insight#514

[vincentarelbundock]

Should we have insight::get_predicted.rma? (I know nothing about these models, so I probably can't help. Sorry.)

[bwiernik]

The major headache currently is that metafor doesn't currently accept data frames as input in its predict method. wviechtb/metafor#48

[wviechtb]

Will try to tackle this soon.

[bwiernik]

Should we have insight::get_predicted.rma? (I know nothing about these models, so I probably can't help. Sorry.)

That's the PR I link above

[vincentarelbundock]

Awesome

[strengejacke]

How can we work on that PR? The branch isn't published, and I cannot pull?

[bwiernik]

Nothing special about it? What happens when you try to checkout origin/metafor?

[strengejacke]

It says I need to fetch, but I can't because the branch isn't published. When I want to publish, it says I can't because I need to fetch latest files. Doesn't work from within vscode nor GitHub desktop app.

[bwiernik]

I'm not sure what that means because the branch is definitely published. I'll take a look when I am at a computer