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---
title: An analysis of iris
---
```{.R results=FALSE echo=FALSE}
options(md_formatter = format_copypaste)
```
Introduction
-------------------------
From the help page of the iris data set:
> This famous (Fisher's or Anderson's) iris data set 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 of iris. The species are _Iris setosa_,
> _versicolor_, and _virginica_.
Descriptives
--------------------------
The table below shows for each of the iris species the mean value of the colums in the data set.
```{.R #table fun=output_table caption="Mean values for each of the properties for each of the iris species."}
aggregate(iris[1:4], iris["Species"], mean)
```
```{.R #figure fun=output_figure
caption="Relation between sepal length and width for the different iris species."
name="iris" height=6 width=8 units="in" res=150 echo=TRUE}
pal <- hcl.colors(3, "Dark2")
plot(iris$Sepal.Width, iris$Sepal.Length, pch = 20,
col = pal[iris$Species], xlab = "Sepal Width",
ylab = "Sepal Length", bty = 'n', las = 1)
legend("topright", legend = levels(iris$Species),
fill = pal, bty = 'n', border = NA)
```
Species prediction
---------------------------------
```{.R}
library(MASS)
m <- lda(Species ~ Sepal.Width + Sepal.Length, data = iris)
p <- predict(m)
predicted_species <- p$class
table(predicted_species, iris$Species)
```
This model predicts in `round(mean(predicted_species==iris$Species)*100)`{.R}% of the
cases the correct species. However, this is mainly for *setosa* for the other species the
model predicts the correct species only for
`sel<-iris$Species!="setosa";round(100*mean(predicted_species[sel] == iris$Species[sel]))`{.R}% of
the records.
```{.R results=FALSE echo=FALSE}
options(md_formatter = NULL)
```
|
