search for: buysk

...19324> I don't really understand how this is stored, and in particular, how I should handle it so as to include the function f1 in a package. I would like the users to be able to load the package and use f1 directly, rather than re-generate it using approxfun. Thanks, Steve --- Steve Buyske (rhymes with "nice key") Associate Research Professor Department of Statistics & Biostatistics Rutgers University Hill Center, 110 Frelinghuysen Rd Piscataway, NJ 08854 buyske at stat.rutgers.edu

...combining C and D, eats up all my memory, while glmmPQL(y ~ A + B, random = ~ 1 | CD, family = binomial) doesn't seem like the model I want. Perhaps this model is too hard to fit, but before I quit this approach I want to make sure that I'm not just coding it incorrectly. Thanks, Steve Buyske

I am involved in a study where, as in most of life, men demonstrate themselves to be recalcitrant. So while we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that

Dear R-users Could anyone tell me which library contains a function to compute polycoric correlations? I wonder the same question was asked a while ago, but I could not locate the mail in the R-help archives. Sorry for bothering you. Sincerely ------------------------ Hiroto Miyoshi ???? h_m_ at po.harenet.ne.jp

Hi the list. Is there a bug in mmlcr package ? The following code does not compile: mmlcrTest <- function(dataW){ dataL <- reshape(dataW,idvar="id",timevar="T",varying=list("T0","T1","T2"),direction="long",v.names="score") resultR <- mmlcr(outer= ~ 1 | id, components = list(list(formula =

I have a dataset with 25 subjects and 25 items. For each subject-item combination, there's a 0/1 score for two parts, A and B. I'm thinking of this as a set of 2 x 2 tables, 25 x 25 of them. I'd like to fit a log-linear model to this data to test the independence of the A and B scores. If I ignore the subject and item parts, the following works just fine: glm(count ~ A * B,