metafor (version 2.4-0)

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, …)

Arguments

object

an object of class "rma".

optionally more fitted model objects.

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 method="REML" and FALSE otherwise.

k

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

correct

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

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.

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. https://www.jstatsoft.org/v036/i03.

See Also

rma.uni, rma.mh, rma.peto, rma.glmm, rma.mv, anova.rma

Examples

Run this code
# NOT RUN {
### meta-analysis of the log risk ratios using a random-effects model
res1 <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
            data=dat.bcg, method="ML")

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

fitstats(res1, res2)

logLik(res1)
logLik(res2)

deviance(res1)
deviance(res2)

AIC(res1, res2)
AIC(res1, res2, correct=TRUE)
BIC(res1, res2)
# }

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