Compute Evidence Ratio Between Two Models
This function compares two models of a candidate model set based on
their evidence ratio (i.e., ratio of model weights). The default
computes the evidence ratio of the model weights between the top-ranked
model and the second-ranked model. You must supply a model selection
table of class aictab, bictab, boot.wt,
dictab, ictab as the first argument.
evidence(aic.table, model.high = "top", model.low = "second.ranked")
aic.table |
a model selection table of class |
model.high |
the top-ranked model (default), or alternatively, the name of another model as it appears in the model selection table. |
model.low |
the second-ranked model (default), or alternatively, the name of a lower-ranked model such as it appears in the model selection table. |
The default compares the model weights of the top-ranked model to
the second-ranked model in the candidate model set. The evidence ratio
can be interpreted as the number of times a given model is more
parsimonious than a lower-ranked model. If one desires an evidence
ratio that does not involve a comparison with the top-ranking model, the
label of the required model must be specified in the model.high
argument as it appears in the model selection table.
evidence produces an object of class evidence with the
following components:
Model.high |
the model specified in |
Model.low |
the model specified in |
Ev.ratio |
the evidence ratio between the two models compared. |
Marc J. Mazerolle
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.
##run example from Burnham and Anderson (2002, p. 183) with two
##non-nested models
data(pine)
Cand.set <- list( )
Cand.set[[1]] <- lm(y ~ x, data = pine)
Cand.set[[2]] <- lm(y ~ z, data = pine)
##assign model names
Modnames <- c("raw density", "density corrected for resin content")
##compute model selection table
aicctable.out <- aictab(cand.set = Cand.set, modnames = Modnames)
##compute evidence ratio
evidence(aic.table = aicctable.out, model.low = "raw density")
evidence(aic.table = aicctable.out) #gives the same answer
##round to 4 digits after decimal point
print(evidence(aic.table = aicctable.out, model.low = "raw density"),
digits = 4)
##example with bictab
## Not run:
##compute model selection table
bictable.out <- bictab(cand.set = Cand.set, modnames = Modnames)
##compute evidence ratio
evidence(bictable.out, model.low = "raw density")
## End(Not run)
##run models for the Orthodont data set in nlme package
## Not run:
require(nlme)
##set up candidate model list
Cand.models <- list()
Cand.models[[1]] <- lme(distance ~ age, data = Orthodont, method = "ML")
##random is ~ age | Subject
Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont,
random = ~ 1, method = "ML")
Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont, random = ~ 1,
method = "ML")
##create a vector of model names
Modnames <- paste("mod", 1:length(Cand.models), sep = " ")
##compute AICc table
aic.table.1 <- aictab(cand.set = Cand.models, modnames = Modnames,
second.ord = TRUE)
##compute evidence ratio between best model and second-ranked model
evidence(aic.table = aic.table.1)
##compute the same value but from an unsorted model selection table
evidence(aic.table = aictab(cand.set = Cand.models,
modnames = Modnames, second.ord = TRUE, sort = FALSE))
##compute evidence ratio between second-best model and third-ranked
##model
evidence(aic.table = aic.table.1, model.high = "mod1",
model.low = "mod3")
detach(package:nlme)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.