Extract the Model Coefficient Table from 'permutest.rma.uni' Objects

Function to extract the estimated model coefficients, corresponding standard errors, test statistics, p-values (based on the permutation tests), and confidence interval bounds from objects of class "permutest.rma.uni".

# S3 method for permutest.rma.uni
coef(object, ...)

Arguments

object

an object of class "permutest.rma.uni".

...

other arguments.

Value

A data frame with the following elements:

estimate

estimated model coefficient(s).

se

corresponding standard error(s).

zval

corresponding test statistic(s).

pval

p-value(s) based on the permutation test(s).

ci.lb

lower bound of the (permutation-based) confidence interval(s).

ci.ub

upper bound of the (permutation-based) confidence interval(s).

When the model was fitted with test="t", test="knha", test="hksj", or test="adhoc", then zval is called tval in the data frame that is returned by the function.

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

See also

permutest for the function to conduct permutation tests and rma.uni for the function to fit models for which permutation tests can be conducted.

Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### fit mixed-effects model with absolute latitude and publication year as moderators
res <- rma(yi, vi, mods = ~ ablat + year, data=dat)

### carry out permutation test
set.seed(1234) # for reproducibility
sav <- permutest(res)
#> Running 1000 iterations for an approximate permutation test.
coef(sav)
#>             estimate          se       zval  pval        ci.lb        ci.ub
#> intrcpt -3.545505079 29.09587983 -0.1218559 0.907 -60.57238164 53.481371479
#> ablat   -0.028011275  0.01023404 -2.7370689 0.016  -0.04806963 -0.007952924
#> year     0.001907557  0.01468382  0.1299088 0.901  -0.02687219  0.030687307