Understanding mi.anova output of mice function in R, miceadds package

Question

I have a dataframe with missing data which I am imputing with mice.

I do not fully understand the output I am getting. SSQ = Sum of Squares. But which sum of squares? Residual sum of squares? Or Total sum of squares - residual sum of squares? I am assuming the latter, but am not sure.

df1 makes sense - it's the number of groups within each variable -1. df2 I don't understand. I have 473 variables, and 20 imputations in mice. But even 473 x 20 does not equal the 5 million in the first column below!!

Also, does the eta2 refer to a result of a one-way anova, and the partial-eta2 refer to the result of my multiway anova?

What is the bottom residual?

Thank you so much for any and all advice!!

    imput <- mice(bdd, seed=1, pred = pred1, meth = meth1, m=20, print = FALSE)
    > MAnoAP<-mi.anova(mi.res=imput,formula="AP~sexe+stage+connaissances.adaptees+temps.entourage+reconnaissance.entourage+reconnaissance.superieurs")
    Univariate ANOVA for Multiply Imputed Data (Type 2)  

    lm Formula:  AP~sexe+stage+connaissances.adaptees+temps.entourage+reconnaissance.entourage+reconnaissance.superieurs
    R^2=0.092 
    ..........................................................................
    ANOVA Table 
                                      SSQ df1        df2 F value  Pr(>F)    eta2 partial.eta2
    gender                        265.56286   1 5426186.13  6.7227 0.00952 0.01333      0.01447
    stage                       736.12077   7  276366.62  2.6410 0.00996 0.03695      0.03910
    connaissances.adaptees      425.68167   1   21534.43 10.4479 0.00123 0.02136      0.02299
    temps.entourage             269.14396   2  524732.88  3.3883 0.03377 0.01351      0.01466
    reconnaissance.entourage    109.61170   1 1148306.99  2.7651 0.09634 0.00550      0.00602
    reconnaissance.superieurs    26.02588   1 7299027.16  0.6574 0.41748 0.00131      0.00144
    Residual                  18092.04873  NA         NA      NA      NA      NA           NA

Answer 1

I figured out part of the answer

SSQ = the part of sum of squares that is attributable to that particular factor

Residual = the SSQ residual

still don't understand df2

Answer 2

df2 = degrees of freedom (for the whole sample size)

Answer 3

SSQ = the part of sum of squares that is attributable to that particular factor

Residual = the SSQ residual

df2 = degrees of freedom (for the whole sample size)