# Exercise 4: Numbers of police

### Instructions:

This document contains information, questions, R code, and plots.

Hints and reminders are bold

Questions appear in blue.

Due midnight 25th February 20221

### Rationale:

To complete this exercise you will need to use:

• The theory you have been taught in the lectures
• The R code you have been given in the intro
• The R help pages and google
• Some biological knowledge
• A bit of creativity and problem solving

This week the exercise looks at how model selection can be important for fitting the data, and drawing the right conclusions.

## Resources:

• P60-65 in ‘The New Statistics with R’
• Google (or any other search engine) - especially “diagnostic plots in R”
• Week 6 module

## R this week:

New functions:

• qqnorm() this is a function that produces a normal QQ plot (quantile-quantile). It plots the quantiles from your data (here residuals) against those of a theoretical perfect normal distribution.
• qqline() this function adds a line to the plot from qqnorm() of 1 to 1 match between your data (residuals) and the theoretical normal distribution.
• plot(YOURMODEL, which=4) this produces a cook’s distance plot. plot(YOURMODEL) by default produces 4 diagnostic plots. Using the which=4 argument just gives us the 4th one.
• indexing: to take only parts of an object in R, we use [ ]. If that object has two dimensions e.g. rows and columns, we can take part of it like this: OBJECT[row number, column number]
• to remove a specific row or column put a - in front of the number or name. E.g. OBJECT[-1,] removes the first row of the object.
• MASS::boxcox() this code uses the boxcox() function within the MASS package. It can be used to plot the log-likelihood for parameters based on different power transformations.
• extracting R-squared: summary(model)$r.squared Things to remember: • lm() takes the arguments x, y, and data e.g. lm(y ~ x, data = YOURDATA) x and y here correspond to an explanatory variable (x) and a response (y) • plot() and abline() hint: you can use abline(a=intercept, b=slope) OR abline(YOURMODEL) • install.packages() if you can’t remember go here Part F. • predict() hint: remember to use lm() with a data argument ## The challenge: How many police officers do you need? You are part of the police headquarters team in Chicago. It is predicted to be a very cold weekend (not that cold compared to the last few weeks in Trondheim! But still cold globally), with an average temperature of -10°C. Obviously most of the police officers would like to make the most of the cold weather by going skiing and ice skating. But you also need to keep the public safe. Previous research has shown that approximately 2 police officers (they work in pairs) are needed for every 20 daily crimes. The Chief of Police has asked your team to provide a recommendation for how many officers they need for this cold weekend. Luckily (as always) you already have data on temperature and crime numbers that you can use. It is your job to find out how many police officers you would recommend to be on duty on Saturday. ## Part A: First recommendation The data from last week’s exercise can be found at https://www.math.ntnu.no/emner/ST2304/2019v/Week5/NoFreezeTempCrimeChicago.csv The first step is to import the data and assign it to an object. You can use the whole web link above to import the data. It is a csv file with column names (header) included. R hint You do not need to download the file first: you can read it into R directly, using read.csv() The next step is to plot the data, its good to remind yourselves what it looked like. R hint # Plot the data, include x and y axis labels plot(YOURDATA$TempC, YOURDATA\$Crime, pch=16,
xlab="Temp (degree C)",
ylab="Daily crime number", las=1)

# Run the regression model
model <- lm(Crime ~ TempC, data=YOURDATA)

# Plot the regression line in dark blue
abline(model, col="darkblue")

A1. Using the linear regression model from last week’s exercise, predict the number of daily crimes for an average temperature of -10°C. Report the answer with prediction interval.

Hint1: you will need to use the predict() function, create newdata, and predict with a prediction interval. If you forget, there is a reminder below.

Hint2: for the lm() you will need to use format lm(y ~ x, data = YourData)

R hint
# run the model
model1 <- lm(Crime ~ TempC, data=YOURDATA)

# create newdata
newdata <- data.frame(TempC=-10)

# create the predictions
predictions <- predict(model1, newdata, interval="prediction")

A2. Based on this result how many police officers would you recommend to be on duty? Explain reasons behind your answer. Include prediction intervals.

## Part B: Model checking

You are speaking to a colleague at lunch, discussing this project (because you are very excited about it). Your colleague tells you they have some extra data. They have mean daily crime numbers from temperatures under 0°C. This is just what you needed!

You can find the new complete dataset here:

https://www.math.ntnu.no/emner/ST2304/2019v/Week6/TempCrimeChicago.csv

It is important to import the data and plot it.

B1. Fit a linear regression to the complete dataset including days <0°C and plot the regression line.

R hint

You should be able to do this using the code in Part A but change the dataset and the model name.

B2. Look at your plot of the data and regression line. What do you think of the fit? Are there any problems with using a straight line on this data? Do this just by looking.

You have had a go at checking this model just by looking at the data and the regression line. But there are some more thorough ways you can explicitly check whether the model meets the assumptions of a linear regression.

The four graphs that statisticians typically use are called: Residuals vs fitted, Normal Q-Q, Residuals vs leverage, and cook’s distance

There are some examples of these in the Model checking section of the linear regression webpage click here.

B3. Create a residuals vs fitted plot for the linear model on all the data. Interpret the plot in terms of model fit.

Think about which assumption this plot assesses, what would you expect it to look like if the assumption is met? and how does your plot look different?

R hint
# This is some code to help make a residuals vs fitted plot

# Step 1 take out the residuals from the model
# the model is the result of using lm()

# You can also round them
# create a vector of rounded residuals
CrimeResiduals <- round(residuals(model2),2)

# Step 2, take out the fitted values from the model
# create a vector of rounded fit
CrimeFitted <- round(fitted(model2),2)

# plot the fitted and residuals
# Fitted on X, residuals on Y
plot(CrimeFitted, CrimeResiduals)

# add a horizontal line at 0
# line is grey and dashed (lty=2)
abline(h=0, lty=2, col="grey")

B4. Create a Normal Q-Q plot for the linear model on all the data. Interpret the plot in terms of model fit.

Again: Think about which assumption this plot assesses, what you expect it to look like if the assumption is met, and how your data differs from what you expect.

R hint
# use the residuals you calculated above

# now create the Normal Q-Q plot
qqnorm(CrimeResiduals)
# add the ideal line
qqline(CrimeResiduals)

You should be able to do this using the R section at beginning of this document and the code for Question B3.

B5. Plot and Interpret the Cook’s distance plot for your model. What does it tell you that the Residuals vs fitted and Normal Q-Q plots did not?

R hint
# Code to make cook's distance plot
# this is done by using the plot function (which would
# create 4 plots of the model) and choosing just the
# last one (which=4). A bit of a cheat to a nice plot.

plot(model2, which=4)

But try using the R help at the beginning of this document first!

B6. Based on your assessments of the model checking plots, how could you improve the model fit for these data? (suggest at least 2 things you could do - try not to cheat)

If you see your answer to B6 was wrong once you open part C below - don’t just change it, try adding an explanation for why you got it wrong and how seeing the answer changed what you look for in future. (You could also have noticed something we missed!)

## Part C:

Only open once you have answered B6.

The colleague that gave you the extra data has come back to see how you are getting on. They suggest that the main assumption not being met is linearity. A straight line does not seem to capture the data because it is curved. There are also some outliers. The outliers are values of 900 and 1200 crimes per day for two cold days close to -20°C

Your team decides to remove the outliers. You have reason to believe they might be typos/incorrect data.

C1. What are some positives and negatives of removing outliers? e.g. What things should you consider when removing them?

C2. Remove the outliers from your data and plot again.

The outliers are at rows 4 and 7. You can combine these numbers using the function c(,). If you are unsure what to do, look at the R help section at the beginning of this document or Google “how to remove rows in R” and see what you get.

The data is still curved. So, you will want to use a transformation of the response variable or a polynomial (square or cube etc). But which one?

You can use Box-Cox to indicate what kind of transformation might help with improve the linear regression. The plot shows the likelihood for different powers of transformation. E.g. 2 is a squared transformation, 3 is cubic etc.

R extra information: to ensure you use a function from a particular package you have loaded you can write the function as PackageName::FunctionName. There is an example of this below.

# You might need to install the package MASS
# install.packages("MASS")

# Run the boxcox function
# lambda = the power transformation
MASS::boxcox(YOURMODEL, lambda = seq(1,4, length=30))

Box-Cox suggests that a quadratic (x2) transformation. You could either transform the response variable OR add a quadratic term as an explanatory variable. You choose to try the second and add the quadratic term.

C3. Add the quadratic term to your model and run again. (show code for this answer)

When you add a quadratic variable, remember to keep the original variable in the model too!

R hint

To add a quadratic effect you need to use the following format:

I(variable^2), where variable = your variable name e.g. TempC.

You then put this quadratic term after your original variable separated by a +. e.g. lm(Y~ X + I(X^2), data = YOURDATA)

I’m still confused
# Create a linear model
# to add a quadratic (or any power) term you must right the
# explanatory variable as I(variable^2) AND keep in the original
# explanatory variable. See example below.
# We need both the linear and quadratic components.

model3 <- lm(Crime ~ TempC + I(TempC^2), data = NoOutliers)

C4. Plot a new residuals vs fitted plot for the new model from C3.

C5. Look at the new Residuals vs Fitted plot. What do you think of this new model? Has it improved the fit?

C6. Now predict the number of crimes for a day of -10°C from the new model from C3. Does this change your recommendation for the number of police needed? If so, how?

## Part D: Reflection

D1. Which model was better at explaining variation in crime? Work out the R squared for the models from Questions B1 and C3. How much of the variance in daily crime numbers does each model explain? Compare them.

The code to do this is at the beginning of this document.

D2. Think about the biological context of the results. Why could there be a quadratic relationship between daily crime numbers and temperature? How could you try to find out what the reasons are? E.g. new studies or data you would need.

## Part E: Feedback

E1. How do you think this exercise went? What do you think your group did well, what are you less sure about? (2 examples of each)

E2. What do you think you improved from last week?

E3. Are there any concepts you are very unsure of?

E4. What would you like feedback on this week?

1. if this is after 2022, you will be given extra time to invent a time machine↩︎