similar to: A faster way to calculate Trace?

I want to try to get a result of element wise addition between a vector and a list. It can be done with "for statement." In order to reducing computing time, I have tried to avoid "for state." If anybody give me an idea, I would apprecite it much. for example, with a & b as below lines, a<- list(c(1,3),c(1,2),c(2,3)) b<-c(10,20,30) I would like to have a list (like

Hello, I work on large matrices and found something interesting. For multiplication of matrices, the order has a huge influence on computing time when one of them is a sparse matrix. In the below example, M is a full matrix and A is a sparse matrix in a regular matrix class. A %*% M takes much more time than M %*% A; moreover, t(t(M) %*% t(A)) is much faster than A %*% M with same result. I

Hello, I was trying to build R from source on Windows XP. I installed software which are mentioned from the follow web page http://www.murdoch-sutherland.com/Rtools/ (Last accessed on Nov. 13th, 2006) . Unfortunately, I got error messages whenever I run 'make all recommended' without modifying 'MkRules' file. I have removed software and reinstalled them several times but I still

Now, I am working with some design matrices. My problem is to set "contrasts" option. Now I want to use "contr.sum" as the option, and it works properly. However, this option sets the last element of a factor as -1. For example, if I have a factor which has 5 elements and want to use the "contr.sum" option, the 5th elements is always set to -1. I want set -1 to other

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

Hello I can construct a correlation matrix from an (ordered) vector of correlation coefficients as follows: x <- c(0.1,0.2,0.3,0.4,0.5) n <- length(x) cmat <- diag(rep(0.5,n)) cmat[lower.tri(cmat,diag=0)] <- x cmat <- cmat+t(cmat) But how to do the reverse operation, i.e. produce x from cmat? Thanks for help, Serguei Kaniovski [[alternative HTML version deleted]]

printCoefmat(signif.legend =FALSE) does not work properly. The option "signif.legend = FALSE" is ignored as shown in the example below. cmat <- cbind(rnorm(3, 10), sqrt(rchisq(3, 12))) cmat <- cbind(cmat, cmat[,1]/cmat[,2]) cmat <- cbind(cmat, 2*pnorm(-cmat[,3])) colnames(cmat) <- c("Estimate", "Std.Err", "Z value", "Pr(>z)") #

I try to code the ULS factor analysis descrbied in ftp://ftp.spss.com/pub/spss/statistics/spss/algorithms/ factor.pdf # see PP5-6 factanal.fit.uls <- function(cmat, factors, start=NULL, lower = 0.005, control = NULL, ...) { FAfn <- function(Psi, S, q) { Sstar <- S - diag(Psi) E <- eigen(Sstar, symmetric = TRUE, only.values = TRUE) e <- E$values[-(1:q)] e <-

Hi, In the data below, "case" represents cases, "x" binary states. Each "case" has exactly 9 "x", ie is a binary vector of length 9. There are 2^9=512 possible combinations of binary states in a given "case", ie 512 possible vectors. I generate these in the order of the decimals the vectors represent, as:

My data looks like this: > data name G_hat_0_0 G_hat_1_0 G_hat_2_0 G_0 G_hat_0_1 G_hat_1_1 G_hat_2_1 G_1 1 rs0 0.488000 0.448625 0.063375 1 0.480875 0.454500 0.064625 1 2 rs1 0.002375 0.955375 0.042250 1 0.000000 0.062875 0.937125 2 3 rs2 0.050375 0.835875 0.113750 1 0.877250 0.115875 0.006875 0 4 rs3 0.000000 0.074750 0.925250 2 0.897750 0.102000

I have data that looks like YC Age Num 82 11 2 83 10 0 84 9 8 85 8 21 86 7 49 87 6 18 88 5 79 89 4 28 90 3 273 91 2 175 with a program mod1=lm(log(Num+1)~YC, data=box44) plot(log(Num+1)~YC, data=box44, pch=19, xlab="Year Class", ylab="Loge Number at age", ylim=c(0,6), xlim=c(91,82)) abline(lm(log(Num+1)~YC), col="blue", lwd=2) summary(mod1) I need to

Dear R users: I have a problem about catch the value from function. I have following two functions (part): fbolus1 <- function() {......... par<-data.frame(Parameter=c("kel","Vd"),Initial=c(0)) check(par) .....} check<-function(par) { if (par[ ,2] <= 0){ cat("\nEnter again (y/n) ?\n\n") ans<-readline() if (ans == "n" ){

Dear useRs, I'm trying to write a loop to sum my data in the following way: (the second - the first) + (the third - the second) + (the fourth - the third) + ... for each column. So, I wrote something like this:   c <- list()   for(i in 1:ncol(mydata)) {   for(j in 2:nrow(mydata)) {   c[[i]] <- sum(yc[j,i] - yc[(j-1),i])   }}} As for the columns it works pretty fine, but it only

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

I've been playing around with contrasts in lm by specifying the contrasts argument. So, I want to specify a specific contrast to be tested Say: > y _ rnorm(100) > x _ cut(rnorm(100, mean=y, sd=0.25),c(-3,-1.5,0,1.5,3)) > reg _ lm(y ~ x, contrasts=list(x=c(1,0,0,-1))) > coef(reg)[2] x1 -1.814101 I was surprised to see that I get a different estimate for the

Hello, please excuse my ignorance as i am new to R and this might seem trivial to you How can i scale my time series in a way that 90% of the data is in the -0.-9/ +0.9 range? I think i first have to select the 90% closest to the mean and then calculate my scaling parameter on it, then scale the whole series. I think i could use outlier() from outliers package to identify the outer 10%,

A trivial case: executing the following DOS command from the prompt line on a Win2K SP3: %windir%\system32\attrib.exe +R H:\dir can't set the R(eadonly) flag on my home dirs (2.2.7): drwxrwsr-x 4 me users 4096 Nov 27 17:59 . drwxr-sr-x 2 me users 4096 Nov 27 17:59 dir -r--r--r-- 1 me users 0 Nov 27 17:59 empty.txt drwxr-sr-x 2 me users 4096 Nov 27 17:27

Hello, I have error on compiling time I tried on HP-UX 11.00 system with gcc version 3.1 to compile samba source Samba latest version source was dowloaded from www.us1.samba.org/samba/ftp I attached log file with this error thank you. begin 600 samba_make.log.doc M(R!M86ME#0HO=7-R+VQO8V%L+W-A;6)A+V)I;B(@+410241$25(](B]U<W(O M;&]C86PO<V%M8F$O=F%R+VQO8VMS(B

I have a model (run with glm) that has a factor, type. Type has two levels, "general" and "regional". I am trying to get estimates (and SEs) for the model with type="general" and type ="regional" using fit.contrast but I can't get the syntax of the coefficients to use in fit.contrast correct. I hope someone can show me how to use fit.contrast, or some

Dear All, I have seldom needed to use loops in R, but now I need to code a loop with a stride different from one. In the R manual I downloaded I have the example: > xc <- split(x, ind) > yc <- split(y, ind) > for (i in 1:length(yc)) { plot(xc[[i]], yc[[i]]); abline(lsfit(xc[[i]], yc[[i]])) } but in my case I'd like to add a condition so that i varies by 4 from one go