coef.rma: Extract the Model Coefficients and Coefficient Table from...

In wviechtb/metafor: Meta-Analysis Package for R

Extract the Model Coefficients and Coefficient Table from 'rma' and 'summary.rma' Objects

Description

Function to extract the estimated model coefficients from objects of class "rma". For objects of class "summary.rma", the model coefficients, corresponding standard errors, test statistics, p-values, and confidence interval bounds are extracted.

Usage

## S3 method for class 'rma'
coef(object, ...)
## S3 method for class 'summary.rma'
coef(object, ...)

Arguments

object	an object of class "rma" or "summary.rma".
...	other arguments.

Value

Either a vector with the estimated model coefficient(s) or a data frame with the following elements:

estimate	estimated model coefficient(s).
se	corresponding standard error(s).
zval	corresponding test statistic(s).
pval	corresponding p-value(s).
ci.lb	corresponding lower bound of the confidence interval(s).
ci.ub	corresponding upper bound of the 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.

Author(s)

Wolfgang Viechtbauer ([e-mail], https://www.metafor-project.org).

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

rma.uni, rma.mh, rma.peto, rma.glmm, and rma.mv for functions to fit models for which model coefficients/tables can be extracted.

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)

### extract model coefficients
coef(res)

### extract model coefficient table
coef(summary(res))

wviechtb/metafor documentation built on Dec. 4, 2024, 1:03 a.m.