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  3. Hierarchical and Mixed Effects Models in R
  • 1

    Overview and Introduction to Hierarchical and Mixed Models

    Free

    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.

  • 2

    Linear Mixed Effect Models

    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.

  • 3

    Generalized Linear Mixed Effect Models

    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.

  • 4

    Repeated Measures

    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.

Initializing

Exercise

Paired t-test

In the video, you learned how paired t-tests can be more powerful than regular t-tests. During this exercise, you will see an example demonstrating this. The first step will be to simulate data. Similar data might come from a people's weights before or after a drug treatment or amount of money spent by a customer before or after a seeing a commercial.

Simulated data allows you to know the properties of the data and check to see if your model behaves as expected. R comes with many distributions including the normal. Your simulated data will have unequal variance (that is, the standard deviations will be different). The second step will be to analyze the data with both a paired and regular t-test. Last, you will be asked about the results from the paired t-tests.

As part of the first step, you will "set the seed" for R's random number generator. This ensures you get the same numbers each time you run the code and DataCamp's software correctly scores your code.

Instructions 1/3

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  • Set the seed to equal 345659.
  • Set n_ind <- 10 to simulate 10 individuals.
  • Simulate the before group with a mean of 0 and standard deviation of 0.5.
  • Simulate the after group by adding a random vector with a mean 4.5 and standard deviation of 5 to the before vector.