## *9. GLM, and General Non-linear Models
## 9.1 A Taxonomy of Extensions to the Linear Model

## 9.2 Logistic Regression
## 9.2.1 Anesthetic Depth Example
anaes.logit <- glm(nomove ~ conc, family = binomial(link = logit), 
+        data = anesthetic)
summary(anaes.logit)

## 9.3 glm models (Generalized Linear Regression Modelling)
anaes.logit <- glm(nomove ~ conc, family = binomial(link = logit),
                 data=anesthetic)
## 9.3.2 Data in the form of counts
## 9.3.3 The gaussian family
# Dataset airquality, from datasets package
air.glm<-glm(Ozone^(1/3) ~ Solar.R + Wind + Temp, data = airquality)
  # Assumes gaussian family, i.e. normal errors model
summary(air.glm)

## 9.4 Models that Include Smooth Spline Terms
## 9.4.1 Dewpoint Data
dewpoint.lm <- lm(dewpoint ~ bs(mintemp) + bs(maxtemp),
                    data = dewpoint)
options(digits=3)
summary(dewpoint.lm)

## 9.5 Survival Analysis
## 9.6 Non-linear Models

## 9.7 Model Summaries
methods(summary)
9.8 Further Elaborations
## 9.9 Exercises
## 9.10 References