Nothing
## get some data
attach(longley)
## get model data
md <- modelData(y=Employed,
X=cbind(GNP, Armed.Forces))
## get a list of all possible models with this data
res <- exhaustive(md)
res
## now the same, but with cubic splines and algorithm 1:
## get model data
md <- modelData(y=Employed,
X=cbind(GNP, Armed.Forces),
splineType="cubic")
## get a list of all possible models with this data
res <- exhaustive(md,
algorithm="1")
res
## now only compute for two certain model configurations:
configs <- cbind(GNP=c(1L, 3L),
Armed.Forces=c(2L, 3L))
res <- exhaustive(md,
modelConfigs=configs)
## now for generalised response:
## get the model data
md <- glmModelData(y=as.numeric(Employed > 64),
X=cbind(GNP, Armed.Forces),
family=binomial)
## and do the exhaustive search
res <- exhaustive(md,
modelPrior="dependent",
computation=
getComputation(higherOrderCorrection=FALSE,
debug=FALSE))
res$models <- res$models[order(res$models$post, decreasing=TRUE), ]
res
res1 <- exhaustive(md,
computation=
getComputation(higherOrderCorrection=FALSE,
debug=FALSE))
res2 <- exhaustive(md,
computation=
getComputation(higherOrderCorrection=FALSE,
debug=FALSE))
res3 <- exhaustive(md,
computation=
getComputation(higherOrderCorrection=FALSE,
debug=TRUE))
str(res1)
identical(res1, res2)
identical(res1, res3)
## now with offsets:
set.seed(93)
offsets <- rnorm(n=length(Employed))
md <- glmModelData(y=round(Employed / 10),
X=cbind(GNP, Armed.Forces),
family=poisson,
offsets=offsets)
res <- exhaustive(md,
computation=
getComputation(higherOrderCorrection=TRUE,
debug=TRUE))
res
res <- exhaustive(md,
computation=
getComputation(higherOrderCorrection=TRUE,
debug=TRUE))
res
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