similar to: Grouping data.frames

Hello, the included XZ Utils source code contains an incorrect use of the restrict keyword. This leads to data corruption under certain circumstances. For a short discussion of the problem see http://sourceforge.net/projects/lzmautils/forums/forum/708858/topic/3306733 This was fixed in the XZ Utils git repository in commit commit 49cfc8d392cf535f8dd10233225b1fc726fec9ef Author: Lasse

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,

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 ->

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 =

[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 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, 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,

Code like df <- data.frame(x=1:10) y <- 20:29 eval(quote(x+y), env=df) does what you might expect: it looks for x and y in the data.frame, and when it doesn't find y there, it looks in the parent environment. However, sometimes I'd like to construct a single environment out of df, so that I can pass it to nested functions and get the same behaviour. Right now, I get the wrong

Code like df <- data.frame(x=1:10) y <- 20:29 eval(quote(x+y), env=df) does what you might expect: it looks for x and y in the data.frame, and when it doesn't find y there, it looks in the parent environment. However, sometimes I'd like to construct a single environment out of df, so that I can pass it to nested functions and get the same behaviour. Right now, I get the wrong

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

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

#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

Hi everyone check this out [R-1.7.0]: R> f1 <- function(x){x^2} R> f1 -> f2 R> f2(4) [1] 16 R> R> function(x){x^2} -> f3 function(x){x^2} -> f3 R> f3(4) Error: couldn't find function "f3" Why does right assignment "->" work in the first but not the second case? Can anyone else reproduce this? -- Robin Hankin, Lecturer, School of

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

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 =

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)

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 ) ) )