metafor (version 2.0-0)

leave1out: Leave-One-Out Diagnostics for 'rma' Objects

Description

The functions repeatedly fit the specified model, leaving out one observation/study at a time.

Usage

leave1out(x, …)
# S3 method for rma.uni
leave1out(x, digits, transf, targs, progbar=FALSE, …)
# S3 method for rma.mh
leave1out(x, digits, transf, targs, progbar=FALSE, …)
# S3 method for rma.peto
leave1out(x, digits, transf, targs, progbar=FALSE, …)

Arguments

x

an object of class "rma.mh", "rma.peto", or "rma.uni".

digits

integer specifying the number of decimal places to which the printed results should be rounded (if unspecified, the default is to take the value from the object).

transf

an optional argument specifying the name of a function that should be used to transform the model coefficients and interval bounds (e.g., transf=exp; see also transf). If unspecified, no transformation is used.

targs

optional arguments needed by the function specified under transf.

progbar

logical indicating whether a progress bar should be shown (the default is FALSE).

…

other arguments.

Value

An object of class "list.rma". The object is a list containing the following components:

estimate

estimated coefficients of the model.

se

standard errors of the coefficients.

zval

test statistics of the coefficients.

pval

p-values for the test statistics.

ci.lb

lower bounds of the confidence intervals for the coefficients.

ci.ub

upper bounds of the confidence intervals for the coefficients.

Q

test statistics for the tests of heterogeneity.

Qp

p-values for the tests of heterogeneity.

tau2

estimated amounts of (residual) heterogeneity (only for random-effects models).

I2

values of \(I<U+00B2>\) (only for random-effects models).

H2

values of \(H<U+00B2>\) (only for random-effects models).

The "list.rma" object is formated and printed with print.list.rma.

Details

The model specified via x must be a model without moderators (i.e., either a fixed- or a random-effects model and not a fixed-effects with moderators or mixed-effects model).

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.

Viechtbauer, W., & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1, 112--125.

See Also

rma.uni, rma.mh, rma.peto

Examples

# NOT RUN {
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### random-effects model
res <- rma(yi, vi, data=dat)

### cumulative meta-analysis
leave1out(res)
leave1out(res, transf=exp)

### meta-analysis of the (log) risk ratios using the Mantel-Haenszel method
res <- rma.mh(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### cumulative meta-analysis
leave1out(res)
leave1out(res, transf=exp)

### meta-analysis of the (log) odds ratios using Peto's method
res <- rma.peto(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### cumulative meta-analysis
leave1out(res)
leave1out(res, transf=exp)
# }