mod.table | R Documentation |
The function provides a table to compare fitted variance-covariance (VCOV) mixed models by AIC, AICc, BIC and LogLik. The restricted log-likelihood (LogLik) statistics for different models can be compared by Chi-square test, while Akaike information criterion (AIC), corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC) are in the smaller-is-better form.
mod.table(modelList)
modelList |
a |
The function returns a table to compare the fitted variance-covariance (VCOV) mixed models to the same data based on information criteria. The smaller AIC, AICc or BIC, the better fit. Also, LogLik value is included. AICc is calculated according to the formula AIC + 2*npar*(nobs/(nobs-npar-2)), where npar represents the number of parameters and nobs the number of observations in the fitted model.
The function returns a data.frame
with rows corresponding to the objects and columns containing the following components:
n |
the number of observations used in the model fit. |
df |
the number of parameters in the fitted model. |
AIC |
Akaike's Information Criterion of the fitted model. |
AICc |
corrected Akaike's Information Criterion of the fitted model. |
BIC |
Bayesian Information Criterion of the fitted model. |
LogLik |
log-likelihood of the fitted model |
Josu G. Alday, Tatiana A. Shestakova, Victor Resco de Dios, Jordi Voltas
Hurvich, C.M. & Tsai, C.L. (1989). Regression and time series model selection in small samples. Biometrika 76: 297-307.
AIC
, BIC
, logLik
## Compare homoscedastic variance-covariance models on Iberian Peninsula # conifer ring chronologies using taxonomic grouping criteria (i.e. Species). data(conifersIP) ModHmSp <- dendro.varcov(TRW ~ Code, varTime = "Year", varGroup = "Species", data = conifersIP, homoscedastic = TRUE) mod.table(ModHmSp)# a data.frame containing information criterion values ## Compare homoscedastic variance-covariance models on Iberian Peninsula conifers # ring chronologies using geographic criteria (ie. Region). ModHmGoe <- dendro.varcov(TRW ~ Code, varTime = "Year", varGroup = "Region", data = conifersIP, homoscedastic = TRUE) mod.table(ModHmGoe)
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