metafor (version 3.8-1)

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

Description

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

Usage

fitstats(object, ...)
# S3 method for rma
fitstats(object, ..., REML)
# S3 method for rma
logLik(object, REML, ...)
# S3 method for rma
deviance(object, REML, ...)
# S3 method for rma
AIC(object, ..., k=2, correct=FALSE)
# S3 method for rma
BIC(object, ...)

Value

For fitstats, a data frame with the (restricted) log-likelihood, deviance, AIC, BIC, and AICc values for each model passed to the function.

For logLik, an object of class "logLik", providing the (restricted) log-likelihood of the model evaluated at the estimated coefficient(s).

For deviance, a numeric value with the corresponding deviance.

For AIC and BIC, either a numeric value with the corresponding AIC, AICc, or BIC or a data frame with rows corresponding to the models and columns representing the number of parameters in the model (df) and the AIC, AICc, or BIC.

Arguments

object

an object of class "rma".

...

optionally more fitted model objects (only for fitstats(), AIC(), and BIC()).

REML

logical to specify 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 (i.e., TRUE if object was fitted with method="REML" and FALSE otherwise).

k

numeric value to specify the penalty per parameter to use. The default (k=2) is the classical AIC. See AIC for more details.

correct

logical to specify whether the regular (default) or corrected (i.e., AICc) should be extracted.

Author

Wolfgang Viechtbauer 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 fit statistics and information criteria can be extracted.

anova.rma for a function to conduct likelihood ratio tests.

Examples

### 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
res1 <- rma(yi, vi, data=dat, method="ML")

### mixed-effects model with absolute latitude and publication year as moderators
res2 <- rma(yi, vi, mods = ~ ablat + year, data=dat, method="ML")

### compare fit statistics
fitstats(res1, res2)

### log-likelihoods
logLik(res1)
logLik(res2)

### deviances
deviance(res1)
deviance(res2)

### AIC, AICc, and BIC values
AIC(res1, res2)
AIC(res1, res2, correct=TRUE)
BIC(res1, res2)