Is it worth it to use lme4 for regression?
In this article, we will look an example that could be analyzed with either a traditional regression approach, using lm (), or a more sophisticated approach using random effects via the lme4 package by Douglas Bates, Martin Maechler and Ben Bolker (2011). And then I’ll pose the question, “is it worth it?”
How is the coef ( ) function used in lme4?
The coef () function in lme4 returns the posterior modes of the βis. That is for a given i, knowledge of the overall distribution of βi, which is N~ (3.06, .122) is updated via Bayes’ rule conditional on the values of yij and xij for j = 1,…,J. Below a few lines of output from the coef () function are shown.
Which is the last line of code for lme4?
The last line of code allows us to view the first 18 records. Notice how βi changes only as the unit id changes, whereas y and x vary at the row level. The cost of having to estimate the βis can be seen by the histograms below, whereas the estimated βis exhibit noticeably more variability. install.packages (“lme4”).
Why are there no covariance terms between random effects?
Because the two random effects are separated into their own terms, no covariance term is included between them. In other words, only the diagonal of the variance-covariance matrix is estimated.
Which is the main function of the lme4 package?
The flagship function of the lme4 package is the lmer () function, a likelihood based system for estimating random effects models. Its formula notation works like lm ()’s for fixed effects, but if you try to run a basic lm () model in it, you’ll get an error message – lmer () needs random effects!
What is the optional DATA frame in predict.lm?
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. A switch indicating if standard errors are required.
Where are the LM and lmer estimates stored?
Recall that the lm () estimates of βi are stored in the vector beta.hat. We can plot them using the code below: (lm estimate of βi, lmer estimate of βi). Notice how there is more variation in the x-axis than the y-axis; lmer’s estimates exhibit less variability than lm.