Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## -----------------------------------------------------------------------------
# Loading BranchGLM package
library(BranchGLM)
# Fitting gamma regression model
cars <- mtcars
# Fitting gamma regression with inverse link
GammaFit <- BranchGLM(mpg ~ ., data = cars, family = "gamma", link = "inverse")
# Forward selection with mtcars
forwardVS <- VariableSelection(GammaFit, type = "forward")
forwardVS
## Getting final model
fit(forwardVS, which = 1)
## -----------------------------------------------------------------------------
# Backward elimination with mtcars
backwardVS <- VariableSelection(GammaFit, type = "backward")
backwardVS
## Getting final model
fit(backwardVS, which = 1)
## -----------------------------------------------------------------------------
# Branch and bound with mtcars
VS <- VariableSelection(GammaFit, type = "branch and bound", showprogress = FALSE)
VS
## Getting final model
fit(VS, which = 1)
## -----------------------------------------------------------------------------
# Can also use a formula and data
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma",
link = "inverse", type = "branch and bound",
showprogress = FALSE, metric = "AIC")
formulaVS
## Getting final model
fit(formulaVS, which = 1)
## ---- fig.height = 4, fig.width = 6-------------------------------------------
# Finding top 10 models
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma",
link = "inverse", type = "branch and bound",
showprogress = FALSE, metric = "AIC",
bestmodels = 10)
formulaVS
## Plotting results
plot(formulaVS, type = "b")
## Getting best model
fit(formulaVS, which = 1)
## ---- fig.height = 4, fig.width = 6-------------------------------------------
# Finding all models with an AIC within 2 of the best model
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma",
link = "inverse", type = "branch and bound",
showprogress = FALSE, metric = "AIC",
cutoff = 2)
formulaVS
## Plotting results
plot(formulaVS, type = "b")
## ---- fig.height = 4, fig.width = 6-------------------------------------------
# Example of using keep
keepVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma",
link = "inverse", type = "branch and bound",
keep = c("hp", "cyl"), metric = "AIC",
showprogress = FALSE, bestmodels = 10)
keepVS
## Getting summary and plotting results
plot(keepVS, type = "b")
## Getting final model
fit(keepVS, which = 1)
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