Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## -----------------------------------------------------------------------------
# Loading in BranchGLM
library(BranchGLM)
# Fitting gaussian regression models for mtcars dataset
cars <- mtcars
## Identity link
BranchGLM(mpg ~ ., data = cars, family = "gaussian", link = "identity")
## -----------------------------------------------------------------------------
# Fitting gamma regression models for mtcars dataset
## Inverse link
GammaFit <- BranchGLM(mpg ~ ., data = cars, family = "gamma", link = "inverse")
GammaFit
## Log link
GammaFit <- BranchGLM(mpg ~ ., data = cars, family = "gamma", link = "log")
GammaFit
## -----------------------------------------------------------------------------
# Fitting poisson regression models for warpbreaks dataset
warp <- warpbreaks
## Log link
BranchGLM(breaks ~ ., data = warp, family = "poisson", link = "log")
## -----------------------------------------------------------------------------
# Fitting binomial regression models for toothgrowth dataset
Data <- ToothGrowth
## Logit link
BranchGLM(supp ~ ., data = Data, family = "binomial", link = "logit")
## Probit link
BranchGLM(supp ~ ., data = Data, family = "binomial", link = "probit")
## -----------------------------------------------------------------------------
# Fitting logistic regression model for toothgrowth dataset
catFit <- BranchGLM(supp ~ ., data = Data, family = "binomial", link = "logit")
Table(catFit)
## -----------------------------------------------------------------------------
# Creating ROC curve
catROC <- ROC(catFit)
plot(catROC, main = "ROC Curve", col = "indianred")
## -----------------------------------------------------------------------------
# Getting Cindex/AUC
Cindex(catFit)
AUC(catFit)
## ---- fig.width = 4, fig.height = 4-------------------------------------------
# Showing ROC plots for logit, probit, and cloglog
probitFit <- BranchGLM(supp ~ . ,data = Data, family = "binomial",
link = "probit")
cloglogFit <- BranchGLM(supp ~ . ,data = Data, family = "binomial",
link = "cloglog")
MultipleROCCurves(catROC, ROC(probitFit), ROC(cloglogFit),
names = c("Logistic ROC", "Probit ROC", "Cloglog ROC"))
## -----------------------------------------------------------------------------
preds <- predict(catFit)
Table(preds, Data$supp)
AUC(preds, Data$supp)
ROC(preds, Data$supp) |> plot(main = "ROC Curve", col = "deepskyblue")
## -----------------------------------------------------------------------------
# Predict method
predict(GammaFit)
# Accessing coefficients matrix
GammaFit$coefficients
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