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.

# 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.

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)
#>      intrcpt        ablat         year 
#> -3.545505079 -0.028011275  0.001907557 

### extract model coefficient table
coef(summary(res))
#>             estimate          se       zval        pval        ci.lb        ci.ub
#> intrcpt -3.545505079 29.09587983 -0.1218559 0.903013130 -60.57238164 53.481371479
#> ablat   -0.028011275  0.01023404 -2.7370689 0.006198931  -0.04806963 -0.007952924
#> year     0.001907557  0.01468382  0.1299088 0.896638598  -0.02687219  0.030687307