# Getting Started

# Your first regression tree

First, install and fire-up R on your computer. Within R, one needs to install the party package by typing

```
install.packages("partykit")
```

and hitting the ENTER key. Once the package is installed, you can load it using

```
library("partykit")
```

```
## Loading required package: grid
```

```
## Loading required package: libcoin
```

```
## Loading required package: mvtnorm
```

```
## Loading required package: rpart
```

Now all party functions are ready to be used, for example the ctree() function for fitting a regression tree to the Ozone data (after removing observations with missing response):

```
### regression
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq,
control = ctree_control(maxsurrogate = 3))
airct
```

```
##
## Model formula:
## Ozone ~ Solar.R + Wind + Temp + Month + Day
##
## Fitted party:
## [1] root
## | [2] Temp <= 82
## | | [3] Wind <= 6.9: 55.600 (n = 10, err = 21946.4)
## | | [4] Wind > 6.9
## | | | [5] Temp <= 77: 18.479 (n = 48, err = 3956.0)
## | | | [6] Temp > 77: 31.143 (n = 21, err = 4620.6)
## | [7] Temp > 82
## | | [8] Wind <= 10.3: 81.633 (n = 30, err = 15119.0)
## | | [9] Wind > 10.3: 48.714 (n = 7, err = 1183.4)
##
## Number of inner nodes: 4
## Number of terminal nodes: 5
```

The tree is represented by an object called airct which can be plotted

```
plot(airct)
```

or used for computing predictions

```
summary(predict(airct))
```

```
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.48 18.48 31.14 42.13 81.63 81.63
```

which can be compared to the actual response values:

```
plot(airq$Ozone, predict(airct))
abline(a = 0, b = 1)
```