Calculating odds-ratios

In the previous exercise, we saw how to compare the effects of a friend's recommendation on sales. However, regression outputs can be hard to describe and sometimes odds-ratios can be easier to use. Using the outputs from the previous exercise, we're going to calculate odds-ratios.

Refresher on odds-ratios:

If an odds-ratio is 1.0, then both events have an equal chance of occurring. For example, if the odds-ratio for a friend's recommendation was 1.0, then a friend would have no influence on a purchase decision.
If an odds-ratio is less than 1, then a friend's recommendation would decrease the chance of a purchase occurring. For example, an odds-ratio of 0.5 would mean a friend's recommendation has odds of 1:2 or 1 purchase occurring for every 2 passes.
If an odds-ratio is greater than 1, then a friend's recommendation would increase the chance of a purchase occurring. For example, an odds-ratio of 3.0 would mean a friend's recommendation has odds of 3:1 or 3 purchases occurring for every 1 passes.

Note on course code: Since this course launched, the broom package has dropped support for lme4::lmer() models. If you try to repeat this on your own, you will need the broom.mixed package, which is on cran.

This exercise is part of the course

Hierarchical and Mixed Effects Models in R

Exercise instructions

Look at the summary() of model_out.
Extract the coefficients from model_out with fixef() and then convert to an odds-ratio by taking exponential. Repeat with confint() to get the confidence intervals.
Calculate the confidence intervals and then exponentiate the effect of friends on a purchase using tidy(). Make sure to set the conf.int and exponentiate parameters.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Run the code to see how to calculate odds ratios
summary( ___) 
exp(___(model_out))
exp(___(model_out))

# Create the tidied output
tidy(model_out, conf.int = ___, exponentiate = ___)

Edit and Run Code

This exercise is part of the course

Hierarchical and Mixed Effects Models in R

AdvancedSkill Level

4.6+

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The first chapter provides an example of when to use a mixed-effect and also describes the parts of a regression. The chapter also examines a student test-score dataset with a nested structure to demonstrate mixed-effects.

Exercise 1: What is a hierarchical model?Exercise 2: Examples of hierarchical datasets Exercise 3: Multi-level student data Exercise 4: Exploring multiple-levels: Classrooms and schools Exercise 5: Parts of a regression Exercise 6: Intercepts Exercise 7: Slopes and multiple regression Exercise 8: Random-effects in regressions with school data Exercise 9: Random-effect intercepts Exercise 10: Random-effect slopes Exercise 11: Building the school model Exercise 12: Interpreting the school model

This chapter providers an introduction to linear mixed-effects models. It covers different types of random-effects, describes how to understand the results for linear mixed-effects models, and goes over different methods for statistical inference with mixed-effects models using crime data from Maryland.

Exercise 1: Linear mixed effect model- Birth rates data Exercise 2: Building a lmer model with random effects Exercise 3: Including a fixed effect Exercise 4: Random-effect slopes Exercise 5: Uncorrelated random-effect slope Exercise 6: Fixed- and random-effect predictor Exercise 7: Understanding and reporting the outputs of a lmer Exercise 8: Comparing print and summary output Exercise 9: Extracting coefficients Exercise 10: Displaying the results from a lmer model Exercise 11: Statistical inference with Maryland crime data Exercise 12: Visualizing Maryland crime data Exercise 13: Rescaling slopes Exercise 14: Null hypothesis testing Exercise 15: Controversies around P-values Exercise 16: Model comparison with ANOVA

This chapter extends linear mixed-effects models to include non-normal error terms using generalized linear mixed-effects models. By altering the model to include a non-normal error term, you are able to model more kinds of data with non-linear responses. After reviewing generalized linear models, the chapter examines binomial data and count data in the context of mixed-effects models.

Exercise 1: Crash course on GLMs Exercise 2: Logistic regression Exercise 3: Poisson Regression Exercise 4: Plotting GLMs Exercise 5: Binomial data Exercise 6: Toxicology data Exercise 7: Marketing example Exercise 8: Calculating odds-ratios

Current Exercise

Exercise 9: Count data Exercise 10: Internet click-throughs Exercise 11: Chlamydia by age-group and county Exercise 12: Displaying chlamydia results

This chapter shows how repeated-measures analysis is a special case of mixed-effect modeling. The chapter begins by reviewing paired t-tests and repeated measures ANOVA. Next, the chapter uses a linear mixed-effect model to examine sleep study data. Lastly, the chapter uses a generalized linear mixed-effect model to examine hate crime data from New York state through time.

Exercise 1: An introduction to repeated measures Exercise 2: Paired t-test Exercise 3: Repeated measures ANOVA Exercise 4: Sleep study Exercise 5: Exploring the data Exercise 6: Building models Exercise 7: Comparing regressions and ANOVAs Exercise 8: Plotting results Exercise 9: Hate in NY state?Exercise 10: Exploring NY hate data Exercise 11: Building the model Exercise 12: Interpreting model results Exercise 13: Displaying the results Exercise 14: Hierarchical models in R review Exercise 15: Conclusion