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Parameters from Meta-Analysis

Source: R/methods_metafor.R
model_parameters.rma.Rd

Extract and compute indices and measures to describe parameters of meta-analysis models.

Usage

# S3 method for rma
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  include_studies = TRUE,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

Arguments

model

Model object.

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

bootstrap

Should estimates be based on bootstrapped model? If TRUE, then arguments of Bayesian regressions apply (see also bootstrap_parameters()).

iterations

The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.

standardize

The method used for standardizing the parameters. Can be NULL (default; no standardization), "refit" (for re-fitting the model on standardized data) or one of "basic", "posthoc", "smart", "pseudo". See 'Details' in standardize_parameters(). Importantly:

  • The "refit" method does not standardize categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as lm.beta) or other software packages (like SPSS). to mimic such behaviours, either use standardize="basic" or standardize the data with datawizard::standardize(force=TRUE) before fitting the model.

  • For mixed models, when using methods other than "refit", only the fixed effects will be standardized.

  • Robust estimation (i.e., vcov set to a value other than NULL) of standardized parameters only works when standardize="refit".

exponentiate

Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use exponentiate = TRUE for models with log-transformed response values. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.

include_studies

Logical, if TRUE (default), includes parameters for all studies. Else, only parameters for overall-effects are shown.

keep

Character containing a regular expression pattern that describes the parameters that should be included (for keep) or excluded (for drop) in the returned data frame. keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in keep will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where model_parameters() returns multiple columns with parameter components, like in model_parameters.lavaan(). Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the $Parameter column of the parameters table to get the exact parameter names.

drop

See keep.

verbose

Toggle warnings and messages.

...

Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like type or parallel are passed down to bootstrap_model().

Value

A data frame of indices related to the model's parameters.

Examples

library(parameters)
mydat <<- data.frame(
  effectsize = c(-0.393, 0.675, 0.282, -1.398),
  stderr = c(0.317, 0.317, 0.13, 0.36)
)
if (require("metafor", quietly = TRUE)) {
  model <- rma(yi = effectsize, sei = stderr, method = "REML", data = mydat)
  model_parameters(model)
}
#> 
#> Loading the 'metafor' package (version 4.4-0). For an
#> introduction to the package please type: help(metafor)
#> 
#> Attaching package: ‘metafor’
#> The following object is masked from ‘package:mclust’:
#> 
#>     hc
#> Meta-analysis using 'metafor'
#> 
#> Parameter | Coefficient |   SE |         95% CI |     z |      p | Weight
#> -------------------------------------------------------------------------
#> Study 1   |       -0.39 | 0.32 | [-1.01,  0.23] | -1.24 | 0.215  |   9.95
#> Study 2   |        0.68 | 0.32 | [ 0.05,  1.30] |  2.13 | 0.033  |   9.95
#> Study 3   |        0.28 | 0.13 | [ 0.03,  0.54] |  2.17 | 0.030  |  59.17
#> Study 4   |       -1.40 | 0.36 | [-2.10, -0.69] | -3.88 | < .001 |   7.72
#> Overall   |       -0.18 | 0.44 | [-1.05,  0.68] | -0.42 | 0.676  |       
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.
# \donttest{
# with subgroups
if (require("metafor", quietly = TRUE)) {
  data(dat.bcg)
  dat <- escalc(
    measure = "RR",
    ai = tpos,
    bi = tneg,
    ci = cpos,
    di = cneg,
    data = dat.bcg
  )
  dat$alloc <- ifelse(dat$alloc == "random", "random", "other")
  d <<- dat
  model <- rma(yi, vi, mods = ~alloc, data = d, digits = 3, slab = author)
  model_parameters(model)
}
#> # 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           |       -0.49 | 0.36 | [-1.20,  0.22] |  -1.35 | 0.176  |       
#> 
#> # other 
#> 
#> 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
#> 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              |       -0.47 | 0.26 | [-0.97,  0.04] | -1.82 | 0.069  |       
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.

if (require("metaBMA", quietly = TRUE)) {
  data(towels)
  m <- suppressWarnings(meta_random(logOR, SE, study, data = towels))
  model_parameters(m)
}
#> This is metaBMA version 0.6.9
#> - Default priors were changed in version 0.6.6.
#> - Since default priors may change again, it is safest to specify priors (even when using the defaults).
#> # Studies 
#> 
#> Parameter                                           | Coefficient |   SE |        95% CI | Weight |                                 Method
#> ------------------------------------------------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1  |        0.38 | 0.20 | [-0.01, 0.77] |  25.59 | Bayesian meta-analysis using 'metaBMA'
#> Goldstein, Cialdini, & Griskevicius  (2008), Exp. 2 |        0.30 | 0.14 | [ 0.04, 0.57] |  53.97 | Bayesian meta-analysis using 'metaBMA'
#> Schultz, Khazian, & Zaleski (2008), Exp. 2          |        0.21 | 0.19 | [-0.17, 0.58] |  27.24 | Bayesian meta-analysis using 'metaBMA'
#> Schultz, Khazian, & Zaleski (2008), Exp. 3          |        0.25 | 0.17 | [-0.08, 0.58] |  34.57 | Bayesian meta-analysis using 'metaBMA'
#> Mair & Bergin-Seers (2010), Exp. 1                  |        0.29 | 0.82 | [-1.33, 1.90] |   1.47 | Bayesian meta-analysis using 'metaBMA'
#> Bohner & Schluter (2014), Exp. 1                    |       -0.12 | 0.25 | [-0.61, 0.36] |  16.25 | Bayesian meta-analysis using 'metaBMA'
#> Bohner & Schluter (2014), Exp. 2                    |       -1.46 | 0.76 | [-2.95, 0.03] |   1.73 | Bayesian meta-analysis using 'metaBMA'
#> 
#> # Meta-Parameters 
#> 
#> Parameter | Coefficient |   SE |        95% CI |    BF |  Rhat |     ESS |                     Prior |                                 Method
#> ---------------------------------------------------------------------------------------------------------------------------------------------
#> Overall   |        0.20 | 0.11 | [-0.02, 0.39] | 0.804 | 1.001 | 4935.00 |   Student's t (0 +- 0.71) | Bayesian meta-analysis using 'metaBMA'
#> tau       |        0.13 | 0.09 | [ 0.03, 0.38] |       | 1.001 | 4026.00 | Inverse gamma (1 +- 0.15) | Bayesian meta-analysis using 'metaBMA'
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a MCMC distribution approximation.
# }