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What are the degrees of freedom in lmertest?
The degrees of freedom in lmerTest::anova are more different from simple anova, but I am still not sure, why they are so large for the within-subject factor/interaction.
How to calculate degrees of freedom in mixed model?
I am aware of the fact that calculating degrees of freedom in mixed-effect models is tricky, but lmerTest::anova is mentioned as one possible solution in the updated ?pvalues topic ( lme4 package). Is this calculation correct? Do the results of lmerTest::anova correctly reflect the specified model?
How does the lmertest package in lme4 work?
The lmerTest package extends the ‘lmerMod’ class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite’s method for approximating degrees of freedom for the t and F tests.
Which is the Satterthwaite freedom for lmertest?
A method, known as Satterthwaite freedom ν such that: F ∼ F q,ν approximately. We hav e implemented their work for the method proposed by Giesbrecht and Burns ( 1985 ). The details of the algorithm are given
What do you mean by degrees of freedom in statistics?
That’s kind of the idea behind degrees of freedom in statistics. Degrees of freedom are often broadly defined as the number of “observations” (pieces of information) in the data that are free to vary when estimating statistical parameters. Now imagine you’re not into hats. You’re into data analysis. You have a data set with 10 values.
When do you use degrees of freedom in a t test?
We know that when you have a sample and estimate the mean, you have n – 1 degrees of freedom, where n is the sample size. Consequently, for a 1-sample t-test, the degrees of freedom equals n – 1. The DF define the shape of the t-distribution that your t-test uses to calculate the p-value.
When do you have 9 degrees of freedom?
It must be a specific number: Therefore, you have 10 – 1 = 9 degrees of freedom. It doesn’t matter what sample size you use, or what mean value you use—the last value in the sample is not free to vary.