Re: [R] Gobbling up a repeating, irregular list of data

It's not clear whether your numbers are tab or space-separated, I will assume space-separated. My lowtech (and not R) solution would be to dump the output into a text file (call it data.in), then run a sed command to first replace two initial spaces from each line, then replace initial spaces with

[R] Gobbling up a repeating, irregular list of data

What would be the sophisticated R method for reading the data shown below into a list? The data is output from a numerical model. Pasting the second block of example R commands (at the end of the message) results in a failure ("Error in scan...line 2 did not have 6 elements"). I no doubt could

[R] Estimate Weighted Euclidean Formula

Given these two kinds of data set, and data set2 was obtained through the weighted Euclidean formula. Can we estimate the weight parameter for each variable in R based on the steepest descent method ? Thanks very much. data set 1: 9 164 78 0 0 32.8 0.148 45 4 134 72 0 0 23.8 0.277 60 5 166 72 19

Re: [R] Helmert contrasts and weighted means

Thanks for the reply. > I suppose you can set up a contrast matrix that would make the intercept > equal to the overall mean, but the definition would depend on the group sizes. Any suggestion on how to set up one? Erlis __ R-help@r-project.org

[ESS] trouble with interactive SAS mode in ESS 13.09-1

I don't seem able to get my code to execute in SAS. Specifically, only the first line in the region seems to be transmitted. Could the fact it is in a file that does not have a .sas extension be a factor? Also, I have problems exiting. BTW, http://ess.r-project.org/Manual/news.html doesn't

Re: [R] Three-component Negative Binomial Mixture: R code

On Thu, 10 Nov 2016, danilo.car...@uniparthenope.it wrote: Thank you for your hints, now the goodness of fit test provides me good results, but surprisingly for me the three-component model turns out to be worse than the two-component one (indeed, I focused on the three-component mixture

Re: [R] Helmert contrasts and weighted means

> On 10 Nov 2016, at 16:30 , erlis ruli wrote: > > Here comes the questions. Is it possible to modify Helmert contrasts in order > to use weighted means instead of means of means? I think not. Not in general. I suppose you can set up a contrast matrix that would make

Re: [R] writeLines: Write several lines

Hello, Just read the help page ?paste, in particular the argument collapse. x <- LETTERS[1:9] paste(x, collapse = "") Hope this helps, Rui Barradas Em 10-11-2016 17:14, Ferri Leberl escreveu: Dear All, If I have a vector V consisting of 9 strings — how can I paste them into a single

[R] writeLines: Write several lines

Dear All, If I have a vector V consisting of 9 strings — how can I paste them into a single string without programming a loop? Thank you in advance! __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see

[R] Estimate Weighted Euclidean Formula

Given these two kinds of data set, and data set2 was obtained through the weighted Euclidean formula. Can we estimate the weight parameter for each variable in R based on the steepest descent method ? Thanks very much. data set 1: 9 164 78 0 0 32.8 0.148 45 4 134 72 0 0 23.8 0.277 60 5 166 72 19

[R] Helmert contrasts and weighted means

Hi all! Suppose that we have got a response y and an unbalanced treatment x with three levels or groups. The treatment is unbalanced by design. Indeed, the first group has 3 replications and the other two have two replications each. For instance, in R the data might look like this: y =

Re: [R] Vectorization in a random order

I think you answered your own question. For loops are not a boogeyman... poor memory management is. Algorithms that are sensitive to evaluation sequence are often not very re-usable, and certainly not parallelizable. If you have a specific algorithm in mind, there may be some advice we can

Re: [R] Vectorization in a random order

You are mistaken. apply() is *not* vectorized. It is a disguised loop. For true vectorization at the C level, the answer must be no, as the whole point is to treat the argument as a whole object and hide the iterative details. However, as you indicated, you can always manually randomize the

Re: [R] Vectorization in a random order

nBuyMat <- data.frame(matrix(rnorm(28), 7, 4)) nBuyMat nBuy <- nrow(nBuyMat) sample(1:nBuy, nBuy, replace=FALSE) sample(1:nBuy) sample(nBuy) ?sample apply(nBuyMat[sample(1:nBuy,nBuy, replace=FALSE),], 1, function(x) sum(x)) apply(nBuyMat[sample(nBuy),], 1, function(x) sum(x)) The defaults for

[R] Vectorization in a random order

Is there a way to use vectorization where the elements are evaluated in a random order? For instance, if the code is to be run on each row in a matrix of length nBuy the following will do the job for (b in sample(1:nBuy,nBuy, replace=FALSE)){ } but apply(nBuyMat, 1, function(x)) will be

Re: [R] Three-component Negative Binomial Mixture: R code

Thank you for your hints, now the goodness of fit test provides me good results, but surprisingly for me the three-component model turns out to be worse than the two-component one (indeed, I focused on the three-component mixture because the two-component one exhibits a low p-value). In