Fit Statistics and Information Criteria for 'rma' Objects
Functions to extract the log-likelihood, deviance, AIC, BIC, and AICc values from objects of class "rma".
fitstats(object, ...) ## S3 method for class 'rma' fitstats(object, ..., REML) ## S3 method for class 'rma' logLik(object, REML, ...) ## S3 method for class 'rma' deviance(object, REML, ...) ## S3 method for class 'rma' AIC(object, ..., k=2, correct=FALSE) ## S3 method for class 'rma' BIC(object, ...)
object |
an object of class |
... |
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 |
k |
numeric value specifying the penalty per parameter to use. The default ( |
correct |
logical indicating whether the regular (default) or corrected (i.e., AICc) should be extracted. |
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.
Variance components in the model (e.g., τ² in random/mixed-effects models) are counted as additional parameters in the calculation of the AIC, BIC, and AICc. Also, the fixed effects are counted as parameters in the calculation of the AIC, BIC, and AICc even when using REML estimation.
Wolfgang Viechtbauer wvb@metafor-project.org http://www.metafor-project.org/
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.
### 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.