Contents
How are estimated marginal means based on data?
Estimated marginal means are based on a model – not directly on data. The basis for them is what we call the reference grid for a given model. To obtain the reference grid, consider all the predictors in the model.
How is the effect function used to calculate confidence intervals?
The effect function calculates confidence intervals (CIs) very quickly, but how trustworthy are these confidence intervals?
How are confidence intervals for lmer objects through effects package?
– Cross Validated How trustworthy are the confidence intervals for lmer objects through effects package? Effects package provides a very fast and convenient way for plotting linear mixed effect model results obtained through lme4 package.
Why is the marginal mean different from the ordinary mean?
The big discrepancy between the ordinary mean for percent = 18 and the marginal mean from cell.means is due to the fact that the lowest value receives 3 times the weight as the other two values. The point is that the marginal means of cell.means give equal weight to each cell.
How can I calculate standard errors for adjusted predictions?
We can use the delta method to get the variance of a function of random variable. As model coefficients are themselves random variables, we can use the delta method to get the variance of conditional and marginal means, because they are functions of the model ceofficients.
When to use an equally weighted average in emmeans?
When in doubt about what is being averaged (or how many), first call emmeans with weights = “show.levels” . If weights is a string, it should partially match one of the following: Use an equally weighted average. Weight in proportion to the frequencies (in the original data) of the factor combinations that are averaged over.
What’s the difference between specs and Emm list?
When specs is a list or a formula having a left-hand side, the return value is an emm_list object, which is simply a list of emmGrid objects. Estimated marginal means or EMMs (sometimes called least-squares means) are predictions from a linear model over a reference grid; or marginal averages thereof.