Tidy a(n) rma objectSource:
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for rma tidy( x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, include_studies = FALSE, measure = "GEN", ... )
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to
Logical. Should individual studies be included in the output? Defaults to
Measure type. See
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass
conf.lvel = 0.9, all computation will proceed using
conf.level = 0.95. Two exceptions here are:
tibble::tibble() with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the individual study
The estimate type (summary vs individual study)
# load libraries for models and data library(metafor) df <- escalc( measure = "RR", ai = tpos, bi = tneg, ci = cpos, di = cneg, data = dat.bcg ) meta_analysis <- rma(yi, vi, data = df, method = "EB") tidy(meta_analysis) #> # A tibble: 1 × 6 #> term type estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 overall summary -0.715 0.181 -3.95 0.0000774