In djourd1/RTutorLogit: RTutor problem set Logit

user.name = '' # set to your user name

# To check your problem set, run the 
# RStudio Addin 'Check Problemset'

# Alternatively run the following lines
library(RTutor)
ps.dir = getwd() # directory of this file
ps.file = 'Logit.Rmd' # name of this file
check.problem.set('Logit', ps.dir, ps.file, user.name=user.name, reset=FALSE)

Logit/Probit Regression

Welcome

Exercise Introduction

We illustrate the steps of building, understanding, and checking the fit of a logistic regression model: modeling the decisions of households in Bangladesh to change their source of drinking water when their wells is contaminated by natural arsenic.

In Bangladesh, the surface water is subject to contamination by microbes and people use water from deep wells for home consumption. However, many of the wells used for drinking water are contaminated with natural arsenic, affecting an estimated 100 million people.

Any locality can include wells with a range of arsenic levels, and even if your neighbor's well is safe, it does not mean that yours is safe. However, if your well has a high arsenic level, you can find a safe well nearby to get your water from - if you are willing to walk the distance and your neighbor is willing to share. Note that the amount of water needed for drinking is low enough that adding users to a well would not exhaust its capacity.

A research team visited all the wells and labeled them with their arsenic level as well as a characterization as "safe" (below 0.5 in units of hundreds of micro-grams per liter) or "unsafe" (above 0.5). People with unsafe wells were encouraged to switch to nearby private or community wells or to new wells of their own construction. A few years later, the researchers returned to find out who had switched wells.

We want to understand the factors predictive of well switching among the users of unsafe wells. Our outcome variable is $y_i = 1$ if household $i$ switched to a new well, or $y_i = 0$ if the household continued using its own well.

This document is largely inspired by: Gelman, A. and J. Hill, Data analysis using regression and multilevel/hierarchical models. 2006, Cambridge: Cambridge University Press. 651pp.

All errors remain mainly mine!

Exercise 1 -- Import the data

The data are stored into an Excel file named wells.xlsx. So the first step will be to import these data for analysis with R. For this we will use the command read_excel() of the package readxl.

info("read_excel") # Run this line (Strg-Enter) to show info

Before you start entering your code, you need to press the edit button. This must be done in every first exercise of a chapter and after every optional exercise which you skipped.

Task: Use the command read_excel() to read in the file wells.xlsx and store it into a new variable variable named wells. If you need help how to use read_excel() check the info box above.

If you need further advice, click the hint button, which contains more detailed information. If the hint does not help you, you can always access the solution with the solution button. Here you just need to remove the # in front of the code and replace the dots with the right commands. Then click the check button to run the command.

library(readxl)
# Type your code

The data set includes 3020 observations. We will only look at the first ones listed in the data set. In R this selection can be performed with the function head().

Task: Take a look at the first observations of the data set. To do this just press check.

head(wells)

Notice that if you move your mouse over the header of a column, you will get additional information describing what this column stands for. In general you always have the possibility to look up these descriptions in this problem set. You just have to press data, this will get you to the Data Explorer section. If you press Description in the Data Explorer, you will get more detailed information about all variables in the data set.

Quiz :Variable description

! addonquizVariable description

Exercise 4 Read -- Reading the results

In this session, we learn to read the output of the glm model. The output includes different information that will essential to evaluate the quality of the model, as well as some information about the relations between explainatory and explained variables.

The following Info button is giving you the meaning of all the different sections. Do not hesitate to consult it.

info("Reading the results") # Run this line (Strg-Enter) to show info

Task 1 Write a logit model explaining the variable changed by 2 explanatory variables arsenic and dist100 and save the results into an object called logit.1. Then ask for a summary of the results.

wells <- read_excel("wells.xlsx")
wells$changed <- factor(wells$switch, levels= c(0, 1), labels = c("Kept", "Changed"))
wells$dist100 <- wells$dist/100
# Type your model here

Quiz 1 : Significant coefficients

! addonquizFind significant coefficients

Quiz 2 : Signs of coefficients and interpretation

! addonquizSigns of coefficients

Quiz 3 : Change in model deviance per degrees of freedom

! addonquizDifference between residual and null deviances

djourd1/RTutorLogit documentation built on April 12, 2020, 10:18 a.m.