Extracting coefficients
We often want to know what values a model estimates as coefficients. Although the summary() function provides the model outputs, we might also want to directly access model outputs.
The fixed-effects estimates can be called directly using the fixef() function. The random-effects estimates can be called directly using the ranef() function. We can also extract confidence intervals for the fixed-effects using the function confint().
The broom.mixed package also contains tidy methods for extracting model results from lmer() models, namely the tidy() function. However, these results are more complex and less tidy than many tidy outputs due to the complexity of mixed-effect models.
A lmer model has been fit for you and saved as out.
This is a part of the course
“Hierarchical and Mixed Effects Models in R”
Exercise instructions
- Extract the fixed-effect coefficients using
fixef()with the saved modelout. - Extract the random-effect coefficients using the
ranef()with the saved modelout. - Estimate the 95% confidence intervals using the
confint()function with the saved modelout. - Use the
tidy()withoutandconf.int = TRUEto repeat your previous three code calls with one tidy command.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Extract the fixed-effect coefficients
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# Extract the random-effect coefficients
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# Estimate the confidence intervals
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# Use the tidy function
___This exercise is part of the course
Hierarchical and Mixed Effects Models in R
In this course you will learn to fit hierarchical models with random effects.
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 dataExercise 2: Building a lmer model with random effectsExercise 3: Including a fixed effectExercise 4: Random-effect slopesExercise 5: Uncorrelated random-effect slopeExercise 6: Fixed- and random-effect predictorExercise 7: Understanding and reporting the outputs of a lmerExercise 8: Comparing print and summary outputExercise 9: Extracting coefficientsExercise 10: Displaying the results from a lmer modelExercise 11: Statistical inference with Maryland crime dataExercise 12: Visualizing Maryland crime dataExercise 13: Rescaling slopesExercise 14: Null hypothesis testingExercise 15: Controversies around P-valuesExercise 16: Model comparison with ANOVAWhat is DataCamp?
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