```{r global_options, include=FALSE}
library(knitr)
opts_chunk$set(fig.align="center", fig.height=3, fig.width=4)
```
## In-class worksheet 13
**Feb 27, 2018**
In this worksheet, we will use the libraries tidyverse, plotROC, and ggthemes:
```{r message=FALSE}
library(tidyverse)
theme_set(theme_bw(base_size=12)) # set default ggplot2 theme
library(plotROC)
library(ggthemes)
```
# 1. Working with training and test data sets
We continue working with the biopsy data set:
```{r}
biopsy <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/biopsy.csv")
```
The following code splits the biopsy data set into a random training and test set:
```{r}
train_fraction <- 0.4 # fraction of data for training purposes
set.seed(126) # set the seed to make the partition reproductible
train_size <- floor(train_fraction * nrow(biopsy)) # number of observations in training set
train_indices <- sample(1:nrow(biopsy), size = train_size)
train_data <- biopsy[train_indices, ] # get training data
test_data <- biopsy[-train_indices, ] # get test data
```
Fit a logistic regression model on the training data set, then predict the outcome on the test data set, and plot the resulting ROC curves. Limit the x-axis range from 0 to 0.15 to zoom into the ROC curve. (Hint: Do **not** use `coord_fixed()`.)
```{r}
# model to use:
# outcome ~ clump_thickness + uniform_cell_size + uniform_cell_shape
# Your R code goes here.
```
# 2. Area under the ROC curves
You can calculate the areas under the ROC curves by running `calc_auc()` on a plot generated with `geom_roc()` (see previous worksheet). Use this function to calculate the area under the training and test curve for the model `outcome ~ clump_thickness`. For this exercise, generate a new set of training and test datasets with a different fraction of training data from before.
```{r}
# Your R code goes here.
```
# 3. If this was easy
Write code that combines the AUC values calculated by `calc_auc()` with the correct group names and orders the output in descending order of AUC. (Hint: We have seen similar code in the previous worksheet.)
```{r}
# Your R code goes here.
```
Write code that generates an arbitrary number of random subdivisions of the data into training and test sets, fits a given model, calculates the area under the curve for each test data set, and then calculates the average and standard deviation of these values.
```{r warning=FALSE}
# Your R code goes here.
```