lab08.Rmd to open the template R Markdown file.🛑 This assignment is due Friday, June 17 at 8:59am so that you are not stressed about submitting the lab during the final exam hours.
For each exercise:
Show all relevant code and output used to obtain your response. If you use inline code, make sure we can still see the code used to derive that answer.
Write all narrative in complete sentences, and use clear axis labels and titles on visualizations.
We will use the tidyverse, tidymodels, knitr packages in this lab.
library(tidyverse)
theme_set(theme_bw())
library(tidymodels)
library(knitr)The wreck of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, the widely considered “unsinkable” ocean liner Titanic sank after striking an iceberg on its maiden voyage. Unfortunately, the ship did not carry enough lifeboats for everyone onboard, resulting in thousands of deaths.
The dataset titanic.csv contains the survival status and other attributes of a group of individuals on the Titanic.
pid: passenger IDsurvived: survival status (0 = died, 1 = survived)pclass: passenger class (1 = 1st, 2 = 2nd, 3 = 3rd)sex: sex (male or female)age: age in yearsfare: passenger fare in British poundstitanic <- read_csv("data/titanic.csv")We are interested in investigating if some groups of people were more likely to survive than others in this tragic accident.
Make a new variable pclass_name in titanic which takes the value “1st” if pclass is 1, “2nd” if pclass is 2, and “3rd” if pclass is 3.
Do the following exploratory data analysis and comment on what you observe.
Make a scatterplot of survival status vs. age where each point has different shape and color by pclass_name. Combine legends for color and shape into one. Use geom_jitter(height = 0.1) instead of geom_point() not to have points on top of each other.
Make a scatterplot of survival status vs. ticket fare where each point is colored by sex. Use geom_jitter(height = 0.1) again. Set alpha = 0.7.
Create a 2x2 contingency table where rows represent survival status, columns represent sex, and each cell contains conditional probability of survival / death given each sex. Print the table as a nice kable. Hint: Use pivot_wider(). In a tibble format, the dimension of the resulting table will be 2x3.
Create a 2x3 contingency table where rows represent survival status, columns represent passenger class, and each cell contains conditional probability of survival / death given each class. Print the table as a nice kable. Hint: Use pivot_wider(). In a tibble format, the dimension of the resulting table will be 2x4.
Fit a logistic regression that predicts survival probability from sex, age, and class of passengers. Print the model output tidily.
Based on the regression output, write out the fitted regression using variable names. Hint: Use indicator variables \(I(\cdot)\).
Which predictors are significant at \(\alpha = 0.05\)?
Interpret the coefficients in context of data using “odds for survival”.
augment() function. Hint: Make a new dataset for new passengers. Uncomment the code chunk below and complete it based on the table below to make the new dataset.| pclass | sex | age |
|---|---|---|
| 1 | female | 17 |
| 2 | female | 16 |
| 3 | female | 18 |
| 1 | male | 9 |
| 1 | male | 23 |
| 1 | male | 52 |
| 3 | male | 18 |
# new_data <- tibble(
# age = c(17, 16, 18, 9, 23, 52, 18),
# sex = c("female", ...),
# # either pclass or pclass_name
# )augment() function and create a new column survived_pred that takes a value 1 if the predicted probability is larger than some cutoff probability. Try three cutoff probabilities of 0.3, 0.5, and 0.8. Report sensitivity and specificity at each cutoff value. Suppose you were a decision maker during the incident of the Titanic and needed to send a limited number of lifeboats to rescue the new passengers in Ex 4 (you have information who is where in the ocean). Which cutoff would you prefer in this case and why? Hint: Sensitivity = P(labeled survived | survived), specificity = P(labeled not survived | not survived).| Component | Points |
|---|---|
| Ex 1 | 3 |
| Ex 2 | 15.5 |
| Ex 3 | 15.5 |
| Ex 4 | 4 |
| Ex 5 | 7.5 |
| Workflow & formatting | 4.5 |
Grading notes: