metafor (version 1.4-0)

fitstats.rma: Fit Statistics and Information Criteria for rma Objects

Description

Functions to extract the log likelihood, deviance, AIC, and BIC values from objects of class "rma".

Usage

## S3 method for class 'rma':
fitstats(object, REML=NULL, \dots)
## S3 method for class 'rma':
logLik(object, REML, \dots)
## S3 method for class 'rma':
deviance(object, REML, \dots)
## S3 method for class 'rma':
AIC(object, \dots, k=2)
## S3 method for class 'rma':
BIC(object, \dots)

Arguments

object

an object of class "rma".

REML

logical indicating whether the regular or restricted likelihood function should be used to obtain the fit statistics and information criteria. Defaults to the method of estimation used, that is TRUE if object was fitted with

k

numeric specifying the penalty per parameter to be used. The default k=2 is the classical AIC. See AIC for more details.

...

other arguments.

Value

• For fitstats.rma, a column vector with the (restricted) log likelihood, deviance, AIC, and BIC values. For logLik.rma, an object of class "logLik", providing the (restricted) log likelihood of the model evaluated at the estimated coefficient(s). For deviance.rma, a numeric value with the corresponding deviance. For AIC.rma, a numeric value with the corresponding AIC. For BIC.rma, a numeric value with the corresponding BIC.

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/.

See Also

rma.uni, rma.mh, rma.peto, anova.rma.uni

Examples

### load BCG vaccine data
data(dat.bcg)

### meta-analysis of the log relative risks using a random-effects model
res1 <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, 
            data=dat.bcg, measure="RR", method="ML")

### mixed-effects model with two moderators (latitude and publication year)
res2 <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, mods=cbind(ablat, year),
            data=dat.bcg, measure="RR", method="ML")

fitstats(res1)
fitstats(res2)

logLik(res1)
logLik(res2)

deviance(res1)
deviance(res2)

AIC(res1)
AIC(res2)

BIC(res1)
BIC(res2)