Adjusted Akaike's Information Criterion.
Calculates adjusted and Bayesian Information Criterion for nmm
object
AICc(object, ..., k = 2) ## S3 method for class 'nmm' AICc(object, ..., k = 2) ## Default S3 method: AICc(object, ..., k = 2) ## S3 method for class 'nmm' BIC(object, ..., k = 2)
object |
Fitted |
... |
Not used. |
k |
Multiplication factor. |
a numeric value with the corresponding AIC, AICc, BIC.
library(systemfit) data( ppine , package="systemfit") hg.formula <- hg ~ exp( h0 + h1*log(tht) + h2*tht^2 + h3*elev) dg.formula <- dg ~ exp( d0 + d1*log(dbh) + d2*hg + d3*cr) labels <- list( "height.growth", "diameter.growth" ) model <- list( hg.formula, dg.formula ) start.values <- c(h0=-0.5, h1=0.5, h2=-0.001, h3=0.0001, d0=-0.5, d1=0.009, d2=0.25, d3=0.005) model.sur <- nlsystemfit( "SUR", model, start.values, data=ppine, eqnlabels=labels ) eq_c <- as.character(c(hg.formula, dg.formula)) parl <- c(paste0("h", 0:3),paste0("d", 0:3)) res <- nmm(ppine, eq_c=eq_c, start_v=start.values, par_c=parl, eq_type = "cont", best_method = FALSE) aa <- in2nmm(res, model.sur$b) AICc(res) AICc(aa) AIC(res) AIC(aa) BIC(res) BIC(aa)
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