print.escalc: Print and Summary Methods for 'escalc' Objects

In wviechtb/metafor: Meta-Analysis Package for R

Print and Summary Methods for 'escalc' Objects

Description

Function to print objects of class "escalc" (and to obtain inferences for the individual studies/rows in such an object). \loadmathjax

Usage

## S3 method for class 'escalc'
print(x, digits=attr(x,"digits"), ...)

## S3 method for class 'escalc'
summary(object, out.names=c("sei","zi","pval","ci.lb","ci.ub"), var.names,
        H0=0, append=TRUE, replace=TRUE, level=95, olim, digits, transf, ...)

Arguments

x	an object of class "escalc" obtained with escalc.
object	an object of class "escalc" obtained with escalc.
digits	integer to specify the number of decimal places to which the printed results should be rounded (the default is to take the value from the object).
out.names	character string with four elements to specify the variable names for the standard errors, test statistics, and lower/upper confidence interval bounds.
var.names	character string with two elements to specify the variable names for the observed effect sizes or outcomes and the sampling variances (the default is to take the value from the object if possible).
H0	numeric value to specify the value of the effect size or outcome under the null hypothesis (the default is 0).
append	logical to specify whether the data frame specified via the object argument should be returned together with the additional variables that are calculated by the summary function (the default is TRUE).
replace	logical to specify whether existing values for sei, zi, ci.lb, and ci.ub in the data frame should be replaced. Only relevant when the data frame already contains these variables. If replace=TRUE (the default), all of the existing values will be overwritten. If replace=FALSE, only NA values will be replaced.
level	numeric value between 0 and 100 to specify the confidence interval level (the default is 95; see here for details).
olim	argument to specify observation/outcome limits. If unspecified, no limits are used.
transf	argument to specify a function to transform the observed effect sizes or outcomes and interval bounds (e.g., transf=exp; see also transf). If unspecified, no transformation is used. Any additional arguments needed for the function specified here can be passed via ....
...	other arguments.

Value

The print.escalc function formats and prints the data frame, so that the observed effect sizes or outcomes and sampling variances are rounded (to the number of digits specified).

The summary.escalc function creates an object that is a data frame containing the original data (if append=TRUE) and the following components:

yi	observed effect sizes or outcomes (transformed if transf is specified).
vi	corresponding sampling variances.
sei	corresponding standard errors.
zi	test statistics for testing \mjeqn\textH_0:\; \theta_i = \textH0H_0: \theta_i = H0 (i.e., (yi-H0)/sei).
pval	corresponding p-values.
ci.lb	lower confidence interval bounds (transformed if transf is specified).
ci.ub	upper confidence interval bounds (transformed if transf is specified).

When the transf argument is specified, elements vi, sei, zi, and pval are not included (since these only apply to the untransformed effect sizes or outcomes).

Note that the actual variable names above depend on the out.names (and var.names) arguments. If the data frame already contains variables with names as specified by the out.names argument, the values for these variables will be overwritten when replace=TRUE (which is the default). By setting replace=FALSE, only values that are NA will be replaced.

The print.escalc function again formats and prints the data frame, rounding the added variables to the number of digits specified.

Note

If some transformation function has been specified for the transf argument, then yi, ci.lb, and ci.ub will be transformed accordingly. However, vi and sei then still reflect the sampling variances and standard errors of the untransformed values.

The summary.escalc function computes level% Wald-type confidence intervals, which may or may not be the most accurate method for computing confidence intervals for the chosen effect size or outcome measure.

If the outcome measure used is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the olim argument to enforce those observation/outcome limits (the observed outcomes and confidence intervals cannot exceed those bounds then).

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

escalc for the function to create escalc objects.

Examples

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

### apply summary function
summary(dat)
summary(dat, transf=exp)

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