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For an application, I have formulas defined in terms of a left Kronecker product of matrices, A,B, meaning A \otimes_L B = {A * B[i,j]} -- matrix on the left multiplies each element on the right. The standard kronecker() function is the right Kronecker product, A \otimes_R B = {A[i,j] * B} -- matrix on the right multiplies each element on the left. The example below shows the result of

I sent this in with an old version, but it's in latest version as well. The fix is simple. In the summary.table function, the parameter is calculated incorrectly for a test of independence among all cells when the table is more than 2-way table. Example: Consider X: > X a b c 1 A1 B2 C1 2 A3 BA3 C2 3 A2 B1 C4 4 A1 B2 C3 5 A3 BA3 C2 6 A1 BA3 C1 7 A2 BA3 C2 8 A1

Hi I searched in the list and only found questions without answers e.g. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/19955.html : Is there a way to get the same results with lme as with aov with Error()? Can anybody reproduce the following results with lme: id<-c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5)

=BE=B4=C6=F4=D5=DF=A3=BA =A1=B0=B5=DA=CB=C4=BD=EC=B1=B1=BE=A9=B3=AF=D1=F4=B9=FA=BC=CA=C9=CC=CE=F1= =BD=DA=A1=B1=BD=AB=D3=DA2003=C4=EA10=D4=C2=D4=DA=B1=B1=BE=A9=BE=D9=B0=EC=A1= =A3=BD=EC=CA=B1=A3=AC=B9=FA=BC=D2=C1=EC=B5=BC=C8=CB=A1=A2=CD=E2=B9=FA=D5=FE= =D2=AA=BA=CD=C0=B4=D7=D4=C3=C0=A1=A2=B5=C2=A1=A2=B7=A8=A1=A2=B0=C4=B4=F3=C0=

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

Dear all, A question on modelling proportional data in R. I have a test experiment that was designed in a particular way, and which I can analyse "by hand" to an extent. I am really struggling to get R to give me sensible results in modelling it "properly", so must be doing something wrong here. As background, I conduct a series of experiments and count the

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

R-helpers; I used Proc Model in SAS to fit the following model to data: proc model data = dbsmv; a = a1*F**2; b = b1*F + b2*T + b3*F*T; tph2 = tph1 *((1 - exp(-a*age2)) / (1 - exp(-a*age)))**-b; fit tph2; and yielded the following estimated parameters after iterations: a1 = -0.15943, a2 = -1.8177, b1 = -0.01911, b2

Dear Wiz[R]ds, I am deeply grateful for the help from Duncan Murdoch, Gray Calhoun, and others. We are almost there. For whatever reason, I can't change the symbol from a circle to a triangle in the upright posture plots. Any ideas? I have included the problem in full. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma # concentrations supine and upright # supine datasu <-

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 25.2, 25.7, 26.3, 25.1, 26.4, 19.6,

Friends I'm trying fit a survival model by maximum likelihood estimation using this function: flver=function(a1,a2,b1,b2) { lver=-(sum(st*log(exp(a1*x1+a2*x2)))+sum(st*log(hheft(exp(b1*x1+b2*x2)*t,f.heft))) -(exp(a1*x1+a2*x2)/exp(b1*x1-b2*x2))*sum(-log(1-pheft(exp(b1*x1+b2*x2)*t,f.heft)))) } emv=mle(flver,start=list(a1=0,a2=0,b1=0,b2=0)) where hheft and pheft are functions defined in

On Mon, 15 Dec 2003, jw schultz <jw@pegasys.ws> wrote: > OK, first pass on TODO complete. .... This hardlink bug report is nearly 21 months old... So I took a look at it using 2.5.7. See below. > BUGS --------------------------------------------------------------- > > Fix hardlink reporting 2002/03/25 > (was: There seems

Hi in matlab I defined a function (double gamma, parameters at the end of this mail) as h(i)=((t/d1)^a1)*exp(-(t-d1)/b1)-c*((t/d2)^a2)*exp(-(t-d2)/b2); h=h/norm(h); I do know that norm() in matlab is equal to: sqrt(sum(x^2)) in R so in R I do it like: #function (double gamama) h <- expression((t/d1)^a1*exp(-(t-d1)/b1)-c*(t/d2)^a2*exp(-(t-d2)/b2)) # plot it t <- seq(0, 20000,

Hello, I'd like to simulate data according to an SVAR model in order to demonstrate how other techniques (such as arima) yield biased estimates. I am interested in a 2 variable SVAR with 2 lags (in the notation of the vars vignette, K = 2, P = 2, where B = I_K). I'm using the {vars} package outlined here: http://cran.r-project.org/web/packages/vars/vignettes/vars.pdf I thought that the

I've two data.tables as shown below: *** N = 10 A.DT <- data.table(a1 = c(rnorm(N,0,1)), a2 = NA)) B.DT <- data.table(b1 = c(rnorm(N,0,1)), b2 = 1:N) setkey(A.DT,a1) setkey(B.DT,b1) *** I tried to change my previous data.frame implementation to a data.table implementation by changing the for-loop as shown below: *** for (i in 1:nrow(B.DT)) { for (j in nrow(A.DT):1) { if

Hi all, I'm trying to test a repeated measures model with random effects using the nlme library. Suppose I have two within subjects factors A, B both with two levels. Using aov I can do: aov.1 <- aov(y ~ A*B + Error(S/(A+B)) following Pinheiro and Bates I can acheive the analagous mixed-effects model with: lme.1 <- lme(y~A*B, random=pdBlocked(list(pdIdent(~1),pdIdent(~A-1),

Hi, I've this dataframe: V1 V5 V6 1 MOD13Q1_2000049 0.1723 A1 2 MOD13Q1_2000049 0.1824 B1 3 MOD13Q1_2000049 0.1824 C1 4 MOD13Q1_2000049 0.1774 A2 5 MOD13Q1_2000049 0.1953 B2 6 MOD13Q1_2000049 0.1824 C2 7 MOD13Q1_2000065 0.1921 A1 8 MOD13Q1_2000065 0.1938 B1 9 MOD13Q1_2000065 0.2009 C1 10 MOD13Q1_2000065 0.2035 A2 11 MOD13Q1_2000065 0.2157 B2 12

#I have a data set that looks like this. A bit more complicated actually with # three factor levels but these calculations need to be done on one factor at a #I then have a set of different rates that are applied #to it. #dataset cata <- c( 1,1,6,1,1,2) catb <- c( 1,2,3,4,5,6) doga <- c(3,5,3,6,4, 0) data1 <- data.frame(cata, catb, doga) rm(cata,catb,doga) data1 # start rates #

Dear R users, Is it possible to use a generalized linear model to do a binomial comparison of one list of proportions with a matched list of proportions to test for a difference? So, for example: list 1 list 2 a1 | b1 a2 | b2 3 | 4 7 | 9 6 | 7 5 | 1 9 | 1 3 | 1 I want to compare list 1 with list 2 and the samples