Quick Question
In R, create a logistic regression model to predict “PoorCare” using the independent variables “StartedOnCombination” and “ProviderCount”. Use the training set we created in the previous video to build the model.
Note: If you haven’t already loaded and split the data in R, please run these commands in your R console to load and split the data set. Remember to first navigate to the directory where you have saved “quality.csv”.
quality = read.csv(“quality.csv”)
install.packages(“caTools”)
library(caTools)
set.seed(88)
split = sample.split(quality$PoorCare, SplitRatio = 0.75)
qualityTrain = subset(quality, split == TRUE)
qualityTest = subset(quality, split == FALSE)
Then recall that we built a logistic regression model to predict PoorCare using the R command:
QualityLog = glm(PoorCare ~ OfficeVisits + Narcotics, data=qualityTrain, family=binomial)
You will need to adjust this command to answer this question, and then look at the summary(QualityLog) output.
What is the coefficient for “StartedOnCombination”?
Exercise 1
Numerical Response
Explanation
To construct this model in R, use the command:
Model = glm(PoorCare ~ StartedOnCombination + ProviderCount, data=qualityTrain, family=binomial)
If you look at the output of summary(Model), the value of the coefficient (Estimate) for StartedOnCombination is 1.95230.
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Quick Question
StartedOnCombination is a binary variable, which equals 1 if the patient is started on a combination of drugs to treat their diabetes, and equals 0 if the patient is not started on a combination of drugs. All else being equal, does this model imply that starting a patient on a combination of drugs is indicative of poor care, or good care?
Exercise 2
Poor Care
Good Care
Explanation
The coefficient value is positive, meaning that positive values of the variable make the outcome of 1 more likely. This corresponds to Poor Care.
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