I’m trying to report an effect size for a Linear Mixed-Model we’ve fitted in R. Right now I’m looking at reporting partial eta squared or eta squared. However, to do so I need to calculate the Sums of Squared Error. We’re using the

`lme()`

function, which does not report MSE or effect sizes. I am not the one doing the primary analysis, so I don’t know if we can switch to using the`ez`

package, as described in Omega squared for measure of effect in R?.The

`lmeObject`

returned by the`lme()`

function has some information, but I am not sure which is the most appropriate. When using`anova()`

on the`lmeObject`

, it reports the denominator degrees of freedom for the F-test as 56683, so with a value for MSE I can calculate SSE and reverse-engineer the F-tests to get the SStreatments I need for partial eta squared. I have 2 fixed effects and one random effect (for a repeated-measures design).I’ve looked at

`lmeObject$sigma`

and calculated the sums of the squares of`lmeObject$residuals[,1]`

, but they don’t agree (I’m squaring the`sigma`

and dividing the SS by the degrees of freedom).Any R masters out there that can tell how to calculate MSE or SSE from an

`lmeObject`

?

**Answer**

**Attribution***Source : Link , Question Author : Oliver , Answer Author : Community*