Akaike weights

Description

This function return the Akaike weights for a set of fitted models.

Usage

aicw(x,...)

Arguments

Details

This function compute the Akaike weights for a set of model AIC or AICc. Akaike weights can be used for model comparison and model averaging.

Value

Author(s)

Julien Clavel

References

Burnham K.P., Anderson D.R. 2002. Model selection and multi-model inference: a practical information-theoric approach. New York: Springer-Verlag.

See Also

AIC mvMORPH

Examples

set.seed(1)
# Generating a random tree
tree<-pbtree(n=50)

#simulate the traits
sigma <- matrix(c(0.01,0.005,0.003,0.005,0.01,0.003,0.003,0.003,0.01),3)
theta<-c(0,0,0)
data<-mvSIM(tree, model="BM1", nsim=1, param=list(sigma=sigma, theta=theta))

## Fitting the models
# BM1 - General structure
fit1 <- mvBM(tree, data, model="BM1", method="pic")

# BM1 - No covariations
fit2 <- mvBM(tree, data, model="BM1", method="pic", param=list(constraint="diagonal"))

# BM1 - Equal variances/rates
fit3 <- mvBM(tree, data, model="BM1", method="pic", param=list(constraint="equal"))

results <- list(fit1,fit2,fit3)

# or
# results <- c(AIC(fit1), AIC(fit2), AIC(fit3))

# Akaike weights
aicw(results)

# AICc weights
aicw(results, aicc=TRUE)

# we can compare the MSE...
# mean((fit1$sigma-sigma)^2)
# mean((fit3$sigma-sigma)^2)

Results