search for: axb

...QL, but I wondered if people could imagine a vector-based solution. Imagine a list A[i] of observers who observe some set of events B[j]. Each observer i may observe one or more events, and each event j may have been observed by one or more observers. Thus the data are a lower-triangular array AxB where each cell [i,j] has a zero or one indicating whether observer i saw event j. I am interested in how observers cluster in circuits whereby observer _a_ sees events _1,2,3_, observer _b_ sees events _2,4,5_, observer _c_ sees event _4_, and observer _d_ sees _4,6,7_. Observers a, b, c, d co...

It would not let me alter the resolution in winecf, under the graphics tab. It only let me delete one number from it, so it is around 1024x70 now. Where do i go to set it using text editing? I cannot use the winecf, because that window is too small now(accidentally hit OK).

Hi, I have two matrices, A (axd) and B (bxd). I want to get another matrix C (axb) such that, C[i,j] is the Euclidean distance between the ith row of A and jth row of B. In general, I can say that C[i,j] = some.function (A[i,], B[j,]). What is the best method for doing so? (assume a < b) I have been doing some exploration myself: Consider the following function: get.f, i...

...2,a3) and B (b1,b2,b3); 5 subjects. Factor A is kind of training (creativity) for the subjacts and factor B is before, during and after training. The results of the example: Source SS df sigma2 F A 9.9 2 4.95 3.87 Subject in sample 15.4 12 1.28 between subjects 25.3 14 B 370.7 2 185.35 44.03 (**) AxB 45.6 4 11.4 2.71 BxSubject 101.0 24 4.21 within subjects 517.3 30 Total 542.6 44

...senting the additive effect of A and B. I then tried lmer(count ~ A * B + (AB - 1 | item) + (AB - 1 | subj), family = poisson, data = llform.df) The lmer function (from the lme4 package) only detects a portion of the variance due to item and subject, and finds a highly significant AxB interaction effect that is really only due to the situation that good subjects with easy items tend to get both A and B right and so forth. I'm not sure whether I'm modeling this wrong in lmer, I'm thinking about this wrong, or I'm asking too much (or any subset of the above),...

Hi all, I hope this question is not too trivial. I can't find an explanation anywhere (Stats and R books, R-archives) so now I have to turn to the R-list. Question: If you have a factorial design with two factors (say A and B with two levels each). What does the intercept coefficient with treatment.contrasts represent?? Here is an example without interaction where A has two levels A1 and