similar to: huge array with floats: allocation error

Hi I have the following array: test <- array(c(1:16), dim = c(3,4,3)) test ## I call some enries using an index array test.ind <- array(rbind(c(1,2,1), c(3,3,2)), dim = c(2,3)) test[test.ind] ## suppose I want all values in the 2nd row and 4th col over ## all three 3rd dimensions test[2,4,] how to specify a test.ind array with the last index left with ',' i.e test.ind should be

Hi In R 1.7 the following worked fine: > array(list(),c(2,5)) [,1] [,2] [,3] [,4] [,5] [1,] NULL NULL NULL NULL NULL [2,] NULL NULL NULL NULL NULL now in R 1.8.1 I get the error: Error in rep.int(data, t1) : invalid number of copies in "rep" In addition: Warning message: NAs introduced by coercion thanks for help, I need this possibility for storing objects (lm results) in

Hi I have a matrix with 30 observations and roughly 30000 variables, each obs belongs to one of two groups. With svm and slda I get into memory troubles ('cannot allocate vector of size' roughly 2G). PCA LDA runs fine. Are there any way to use the memory issue withe SVM's? Or can you recommend any other classification method for such huge datasets? P.S. I run suse 9.1 on a 2G RAM

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

Hi I have calculated lots (>1000) of linear models and would like to store each single result as an element of a 3D matrix or a similar storage: something like glm[i][j][k] = lm(...) Since I read that results are lists: Is it possible to define a matrix of type list? Or what do you recommend? Many thanks Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Hi I use sweave for excellent pdf output (thank you- Friedrich Leisch). I have just one problem. Quite often it happens, that the graphics are not at the place where I expect them, but (often on a separate page) later on in the pdf. How can I fix this, means how can I define, that I want a graphic exactly here and now in the document? Many thanks and best regards Christoph -- Christoph

Dear contributors I have tried this experiment: x<-c() for (i in 1:12){ x[i]<-list(cbind(x1[i],x2[i])) #this is a list of 12 couples of time series I am using to perform a test } # that compares them 2 by 2 # ################# #trace statistic test<-data.frame() cval<-array( , dim=c(2,3,12)) for (i in 2:12){ for (k in 1:2){ for (j in 1:3){ result[k,j,i]<-

hi all, i have a simple question. instead of defining my measurements in a static way like ... x <- c(-0.475, -1.553, -0.434, -1.019, 0.395) ... i'd like them to be read from a file ... x <- read.table("07a673ac0cb1f7f8fa293860566f633c/1/raw0.txt", header=FALSE) d1 <- density(x, kernel = "gaussian") with a formatting that looks like: 4.2840000000e-01

Hi I need to define the following c("one group" = class.weight[2], "other group" = class.weight[1]) #class.weight = c(1,2) but I don't like the hard-coded way and would like to use my.group <- array(c("one group", "other group")) but now c(my.group[1] = class.weight[2], my.group[2] = class.weight[1]) gives an error how can I solve this

again, under another subject: sorry, maybe an all too trivial question. But we have power data from J frequency spectra and to have the same range for the data of all our subjects, we just transformed them into % values, pseudo-code: power[i,j]=power[i,j]/sum(power[i,1:J]) of course, now we have a perfect linear relationship in our x design-matrix, since all power-values for each subject sum up

Dear R-users How can I get the eigenvalues out of an lda analysis? thanks a lot christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

Hi I need to overlay two pixmaps (library (pixmap)). One, a pixmapGrey, is the basis, and on this I need to overlay a pixmapIndexed, BUT: the pixmapIndexed has set only some of its "pixels" to an indexed color, many of its pixels should not cover the basis pixmapGrey pixel, means, for this "in pixmapIndexed not defined pixels" it should be transparent. What would you

Dear R experts I have the follwoing data V1 V2 1 -5.8000000 0 2 -4.8000000 0 3 -2.8666667 0 4 -0.8666667 0 5 -0.7333333 0 6 -1.6666667 0 7 -0.1333333 1 8 1.2000000 1 9 1.3333333 1 and I want to know, whether V1 can predict V2: of course it can, since there is a perfect separation between cases 1..6 and 7..9 How can I test, whether this conclusion (being able to assign an

Hello, How I use the function as.svrepdesign without memory.size problems? desenho_npc_JK <- as.svrepdesign(desenho_npc,type="JKn") Error: cannot allocate vector of size 161.3 Mb In addition: Warning messages: 1: Reached total allocation of 1022Mb: see help(memory.size) 2: Reached total allocation of 1022Mb: see help(memory.size) 3: Reached total allocation of 1022Mb:

Hello list, i've got some problems getting a E1 line with a E100P up and running (germany). # cat /proc/zaptel/1 Span 1: WCT1/0 "Digium Wildcard E100P E1/PRA Card 0" HDB3/CCS/CRC4 YELLOW RED "YELLOW RED" sounds not so good. When launching asterisk, enabling pri debug on that span, i see outgoing attempts: ;---------- snip -------- > [00 01 7f ] > Unnumbered

am I right in the assumption, that for calculation of the condition numbers I have to use the correlation matrix of X, and not t(x) %*% x? > e <- eigen(t(x) %*% x) better (x must not have a first column of ones): > e <- eigen(cor(x)) > e$val [1] 6.6653e+07 2.0907e+05 1.0536e+05 1.8040e+04 2.4557e+01 2.0151e+00 > sqrt(e$val[1]/e$val) [1] 1.000 17.855 25.153 60.785 1647.478

Hi Since the 'equal slope' assumption doesn't hold in my data I cannot use a proportional odds model ('Design' library, together with 'Hmisc'). I would like to try therefore a partial proportional odds model Please, could anybody tell me, where to find the code and how to specify such a model ..or any potential alternatives many thanks for your kind help christoph

Hi It seems, I just miss something. I defined treshold <- function(pred) { if (pred < 0.5) pred <- 0 else pred <- 1 return(pred) } and want to use apply it on a vector sapply(mylist[,,3],threshold) but I get: Error in match.fun(FUN) : Object "threshold" not found thanks for help cheers chris -- Christoph Lehmann <christoph.lehmann at gmx.ch>

Hi (1)In polr(), is there any way to calculate a pseudo analogue to the R^2. Just for use as a purely descriptive statistic of the goodness of fit? (2) And: what are the assumptions which must be fulfilled, so that the results of polr() (t-values, etc.) are valid? How can I test these prerequisites most easily: I have a three-level (ordered factor) response and four metric variables. many