similar to: slow computation of mixed ANOVA using aov

Hello, I have a list of data with multiple elements, and each element in the list has multiple variables in it. Here's an example: ### Make the fake data dv <- c(1,3,4,2,2,3,2,5,6,3,4,4,3,5,6) subject <- factor(c("s1","s1","s1","s2","s2","s2","s3","s3","s3",

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->

<ripley at stats.ox.ac.uk> wrote: > Hint 2: in the absence of balance, ...., and lme can do that Would it be possible to make aov like wrappers to the various special lm variants allowing for a uniform syntax for anova? aov(resp~f1*f2+Error(S/(f1*f2))) ## uses lm aov.lme(resp~f1*f2+Error(S/(f1*f2))) ## uses lme aov.rlm(resp~f1*f2+Error(S/(f1*f2))) ## uses rlm ... I'd do it, but I

Dear R-List, (I am not sure whether this list is the right place for my question...) I have a dataframe df.cfa

Hello, FYI, this is a bug http://llvm.org/bugs/show_bug.cgi?id=13324 When compiling the following code for sm_20, func params are by some reason given with .align 0, which is invalid. Problem does not occur if compiled for sm_10. > cat test.ll ; ModuleID = '__kernelgen_main_module' target datalayout = "e-p:64:64-i64:64:64-f64:64:64-n1:8:16:32:64" target triple =

Dear sir, I have a matrix like a<-matrix(c(0,2,0,4,0,6,5,8,0),nrow=3) colnames(a)<-c("F1","F2","F3") rownames(a)<-c("A1","A2","A3") a F1 F2 F3 A1 0 4 5 A2 2 0 8 A3 0 6 0 I want to extract all pairs (rownames, columnames) from which the value in the matrix is 0 The result should be something like this A1, F1 A2,

[AMD Public Use] Hi Oliver, I get rid of the warnings by explicitly type-casting it to struct*, and still get similar results. ####################################################### struct P { float f1; float f2; float f3[3]; float f4; }; void foo(struct P* p1, struct P* p2) { p1->f2 = 1.2; p2->f1 = 3.7; } int callFoo() { struct P p; foo(&p,

Hi, Here's the data we have: > rs[1:5,] probe_id f1 f2 f3 f4 M A f 1 A_68_P20002076 2 58 0 0 1.51778114 6.344453 59 2 A_68_P20002775 22 8 15 0 0.43419304 5.488819 59 3 A_68_P20005791 43 3 0 0 0.05698666 9.830594 59 4 A_68_P20005805 11 34 0 0 1.71076835 6.624038 59 5 A_68_P20006729 16 44 0 0 0.85498261 6.044229 59 I want rs$f be the max of f1, f2, f3,

Hi list, Sorry for the vague title, but here is the scenario. I?m writing an R package, let?s say, ?pkg1?, which contains 3 functions: f1, f2, f3. f2 and f3 are helper functions for f1, i.e. f1 calls f2 which in turn calls f3. f1 <- function(?) { ?. f2() ? } f2 <- function(?){ ? f3(?) ? } f3 <- function(...){ .... } Then, I wrote a new version of f3 and I want to

Hi everyone, I'm fairly new to R, and I don't have a background in statistics, so please bear with me. ;-) I'm dealing with 2^k factorial designs, and I was just wondering if there's any way to analyze more than two factors of a gage R&R study in R. For example, Minitab has an "expanded gage R&R" function that lets you include up to eight additional factors

I'm interested in plotting a y with an x factor as the combination of 2 factors and colour with respect to a third, which the code below does with interaction.plot(). However, this is because I redefine the x to be 1 factor. Is there a way of getting it to plot without redefining it, and ideally to not join up the lines BETWEEN levels a and b, but just join those between after and before for

#I have a dataset with two factor. I want to combine those factors into a single factor and count the number of data values for each new factor. The following gives a comparable dataframe: a <- rep(c("a", "b"), c(6,6)) b <- rep(c("c", "d"), c(6,6)) df <- data.frame(f1=a, f2=b, d=rnorm(12)) df # I use the 'interaction' function to combine

Hi Folks, Can anyone give me the tip I've been groping for with the following question:? mu: kx2x2x2 array of reals corresponding to means of k RVs at the combinations of values (1,2)x(1,2)x(1,2) of dichotomous variables F1,F2,F3 mu prints out as k rows (one for each Xi) of 8 numbers M: N x (3+k) matrix of cases. The first 3 cols are values of

Dear all, Last December there was a thread regarding the famous FAQ 7.21 "How can I turn a string into a variable?" and asking what people want to do with these strings. My, certainly trivial application would be as follows: Assume I have a data.frame containing besides others also the columns f1, f2, ..., fn and I want to create a command like: apply(sapply(list(f1,f2,f3),is.na),2,sum)

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

A strange problem with sem: I downloaded the sem library and then, I specified my simple measurement model (below). I highlighted it and ran it. It ran, but it did NOT tell me "22 lines read". And nothing works after that - it looks like it runs, but it does not produce anything... Did I make a mistake somewhere in the model? (notice, TIME has only 1 indicator - t1, and I fixed t1's

Hi, I have the following table data: f1, f2, f3, f4. I want to compute the counts of unique combinations of f1-f4. In SQL I would just write: SELECT COUNT(*) FROM <table> GROUP BY f1, f2, ..,f4. How to do this in R? Thanks, Nick -- View this message in context: http://www.nabble.com/Group-by-in-R-tp23020587p23020587.html Sent from the R help mailing list archive at Nabble.com.

When adding several logical vectors I expect each vector will be coerced to integers and these vectors will then be added. That doesn't always seem to be the case. For example: > ( f1 <- as.factor ( sample ( "x" , 25 , rep = T ) ) ) [1] x x x x x x x x x x x x x x x x x x x x x x x x x Levels: x > ( f2 <- as.factor ( sample ( "y" , 25 , rep = T ) ) )