search for: ssqs

...n R? Not that I would like to use them, I know they are problematic. I would like to know how to calculate them in order to demonstrate that strange things happen when you use them (for a course for example). I know you can use drop1(lm(), test="F") but for an lm(y~A+B+A:B), type III SSQs are only calculated for the A:B interaction. If I specify lm(y~A+B), it calculates type II SSQ for the main effects (as type III only differs from type II if interactions are included). Thus, this approach doesn't help. Another approach is the Anova(, type="III") function within t...

...of squares is variance # times d.f. # Method 2: from ANOVA residuals rsd_grp <- rsd[index] # extract residuals ssq_rsd <- sum(rsd_grp **2) # compute sum of squares # print mean, variance, and difference between SSQs from the two # methods. write(sprintf("%s: mean=%5.1f var=%5.1f (%.2g)", l, mean(smp), var(smp), ssq_smp-ssq_rsd), "") # ...and it works like expected! But is there a shortcut that would give me # the same result in a one-liner? }

...80 1 130.32 12.425 0.002419 > anova(fm3, fm2) Analysis of Variance Table Model 1: stack.loss ~ 1 Model 2: stack.loss ~ Air.Flow Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F) 1 20 2069.24 2 19 319.12 1 1750.12 104.20 3.774e-09 Note that the SSqs are all the same, but the sequential table compares them to the residual MSq, not to the next-larger model (as there is no test that terms which have already been dropped are not significant). -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://w...

Full_Name: Tanya Logvinenko Version: 1.7.0 OS: Windows 2000 Submission from: (NULL) (132.183.156.125) For unbalanced design, I ran into problem with ANOVA (aov function). The sum of squares for only for the second factor and total are computed correctly, but sum of squares for the first factor is computed incorreclty. Changing order of factors in the formula changes the ANOVA table. For the

How do we specify a contrast interaction matrix for an ANOVA model? We have a two-factor, repeated measures design, with Cue Direction (2) x Brain Hemisphere(2) Each of these has 2 levels, 'left' and 'right', so it's a simple 2x2 design matrix. We have 8 subjects in each cell (a balanced design) and we want to specify the interaction contrast so that: CueLeft>CueRght

I have a challenge that I want to share with the group. This is not homework (but I may assign it as such if I teach the appropriate class again) and I have found one solution, so don't need anything urgent. This is more for fun to see if others can find a better solution than I did. The challenge: I want to read a book in a given number of days. I want to read an integer number of