excercise.Rmd

---
title: "The Iris Data Set"
author: "Fabian Schroeder"
date: "September 13, 2017"
output: pdf_document
bibliography: bibliography.bib
---

```{r setup, include=FALSE}
library("xtable")
library("stargazer")
data("iris")
```

*** 

## Description

This famous dataset [@fisher, @anderson] gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species or iris. The species are *Iris setosa, versicolor*, and *virginica*.  

![Illustration of the Variables of the iris data set.](iris.png)

Iris is a data frame with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.

```{r echo=FALSE}
#summary(iris)
```

```{r echo=FALSE, results="asis"}
options(xtable.comment = FALSE)
xtable::xtable(summary(iris), type = "latex", comment = FALSE, caption = "Summary of the data set")

```

## Scatterplot Matrix

```{r echo=FALSE}
plot(iris)
```

## Logistic Regression Analysis

```{r echo = FALSE, include=FALSE}
iris[['Is.Versicolor']] <- as.numeric(iris[['Species']] == 'versicolor')
iris[['Is.Virginica']] <- as.numeric(iris[['Species']] == 'virginica')
fit.1 <- glm(Is.Versicolor ~ Petal.Length + Sepal.Length, data = iris)
fit.2 <- glm(Is.Virginica ~ Petal.Length + Sepal.Length, data = iris)
#summary(fit)
output <- capture.output(stargazer(fit.1, fit.2, title = 'Regression Results', summary=FALSE, header=FALSE))

```


```{r echo = FALSE, results='asis'}
cat(output)
```

## References