similar to: From vector to a step function

Exec. Summary: step() basically ``fails'' whereas MASS' stepAIC() does work This may not be a bug in the strictest sense, but at least something for the wish list. Unfortunately I have no time currently to investigate further myself but want to be sure this won't be forgotten: The example is using a real data set with 216 observations on 9 variables -- where we have

Hi, I start with an array of booleans: x <- c( TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE ); I want to define an y <- f(x) such that: y <- c( 1, 2, 3, 0, 0, 1, 2, 0, 1 ); In other words, do a cumsum when I see a TRUE, but reset to 0 if I see a FALSE. I know I can do this with a very slow and ugly loop or maybe use apply, but I was hoping there are some R experts out

Dear all, It has been ages since I studied integration in college. Right now I try to recover all this kind of knowledge and then try to understand how integration works. Thus I am doing some first 'experiments' and I would like to request your help and comments. I have the function: p2<-function(x){0.5*(3*x^2-1)} # I found the square of p2 by using some pencil and

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 all, Could you please explain me why > OverloadsTesT [1] 1 0 1 0 0 0 0 0 0 0 > a<-matrix(data=seq(1,10),nrow=10) > a [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 [5,] 5 [6,] 6 [7,] 7 [8,] 8 [9,] 9 [10,] 10 > a[-OverloadsTesT] [1] 2 3 4 5 6 7 8 9 10 the last line does not remove the first and third element and only does the first element.? What I

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

Hello, Consider: f1 <- function(a, b, c, d, ...) { c(list(a = a, b = b, c = c, d = d), list(...)) } > f1(a = 1, b = 2, c = 3, d = 4, more = 5) $a [1] 1 $b [1] 2 $c [1] 3 $d [1] 4 $more [1] 5 Question: I'm guessing there exists a function such that I don't have to list each bound argument separately in the list in 'f1'. It seems like it should be easy but

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,

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

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,

Hi, I have a data frame where one column is a list of lists. I would like to subset the data frame based on membership of the lists in that column and be able to 'denormalise' the data frame so that a row is duplicated for each of its list elements. Example code follows: # The data is read in in this form with the c2 list values in single strings which I then split to give lists: > f1

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

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.

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

#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

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

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)