similar to: [Q] a dummy variable used with sapply

Hi all, I have two basic questions, hope you should help me: 1. How do I save a log file in R with the results? For example, in Stata it can be done by using "log using c:\...\test.txt" 2. How do I display the execution time of one function like the one below: CalculaCorrelacao <- function(construto, n) { library(polycor) for (i in 2:n){ for (j in i:n+1){ x1

Hi All, I am using a specialized aggregation function to reduce a dataset with multiple rows per id down to 1 row per id. My function work perfect when there are >1 id but alters the 'var.g' in undesirable ways when this condition is not met, Therefore, I have been trying ifthen() statements to keep the original value when length of unique id == 1 but I cannot get it to work. e.g.:

Two friend reported me a problem, which I can't solve: (I run R-1.0.0, Debian Linux) They hava a function "corr.matrix" (see end of mail), and when they create a 173x173 matrix with this function V <- corr.matrix(0.3, n=173) V1 <- qr.solve(V) reports: Error in qr(a, tol = tol) : NA/NaN/Inf in foreign function call (arg 1) For n < 173, qr.solve returns the correct

Hi I am trying to plot 100 x 100 matrix data in a level plot. The problem I have is that the x/y -axis label in ticks are jumbled together. Thus I want X/Y axis to contain 10 ticks only, yet still plotting all the 100 data. Is there a way to do it? The code I have below doesn't work. __BEGIN__ # Corr contains 100x100 matrix corr <- cor(t(mat.data), method ="pearson") # Plot

Dear all, I want to find the optimal values of a vector, x (with 6 elements) say, satisfying the following conditions: 1. for all x>=0 2. sum(x)=1 3. x[5]<=0.5 and x[6]<=0.5 For the minimisation I'm using nlminb and to satisfy the first 2 conditions the logistic transformation is used with box constraints for condition 3. However, I don't seem to be able to get the values x

Hi. Does anyone know whether the following error is a result of a bug or a feature? I can eliminate the error by making ML=F, but I would like to see the values of the cut-points and their variance. tmp.vec<-c(0, 0, 0 , 0 ,0 , 1, 0, 2, 0 , 0, 5 ,5 ,3 ,1, 0 , 1, 5, 10, 27, 20, 9, 0, 1, 1, 12, 29, 57, 34, 0, 0, 1, 2, 11, 31, 32) tmp.mat<-matrix(tmp.vec, nrow=7)

All, I have a repeated measures data frame and was wondering if the covariance matrix can be calculated via some created indexing or built-in R function. Specifically, say there are 3 variables, where potassium concentration is measured 6 times on each patient. Patient number (discrete) Time (1 to 6, discrete) Potassium (continuous variable) I want the covariance/correlation matrix for the

I am writing a function in fortran 95, but the intrinsic function MATMUL is not working properly. Here's an example. SUBROUTINE mymult(x,y,res,m,n) IMPLICIT NONE INTEGER :: m,n REAL :: x, y, res DIMENSION :: x(m,n), y(n,m), res(m,m) res = MATMUL(x,y) END SUBROUTINE mymult R CMD SHLIB mat.f95 In R: dyn.load("mat.so") x <-

Hello, I will be very obliged if someone can help me with this statistical R problem: I am trying to do a Pearson correlation on my datasets X, Y with randomization test. My X and Y datasets are pairs. 1. I want to randomize (rearrange) only my X dataset per row ,while keeping the my Y dataset as it is. 2. Then Calculate the correlation for this pair, and compare it to your true

Oops, I just erased all my data using this gizmo that I thought would replace -9 with NA. A) Can I get my tcn5 back? B) How do I do it right next time, I learned my lesson, I'll never do it again, I promise! Anders Corr > for(i in 1:dim(tcn5)[2]){ ##for the number of columns + for(n in 1:dim(tcn5)[1]){ ##for the number of rows + tcn5[is.na(tcn5[n,i]) | tcn5[n,i]

Hi, I am a newbie of R, and playing with the "ifelse" statement. I have the following codes: ## first, for(i in 1:3) { for(j in 2:4) { cor.temp <- cor(iris.allnum[,i], iris.allnum[,j]) if(i==1 & j==2) corr.iris <- cor.temp else corr.iris <- c(corr.iris, cor.temp) } } this code is working fine. I also tried to perform the same thing in another way with "ifelse":

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

Hi all, I'd like to use the Hmisc::summarize function, but it uses a function (FUN) of a single vector argument to create the statistical summaries. Consider an easy case: I'd like to compute the correlation between two variables in my dataframe, grouped according to other variables in the same dataframe. For exemple, consider the following dataframe D: V1 V2 V3 A 1 -1 A 1

hi everyone, Suppose I have three variables a, b, and c each with 10 values. Now I construct a corr matrix for them. Now I want to give the names of columns of corr matrix as a, b, c, i.e. the first column of corr matrix will have name as ‘a’ second column with ‘b’ and so on. Can anyone give me any code by which I can automatically assign the names of columns of corr matrix which are

OK, I got it figured out. I was not keying into a length greater than 1, so: # I added this object and placed it into the iftelse statement: lid <- sum(match(id, st[i], nomatch = 0)) out$var.g[i]<-ifelse(lid ==1, meta$var.g[id==st[i]], aggs(g=g[id==st[i]], n.1= n.1[id==st[i]], n.2 = n.2[id==st[i]], cor)[2]) #full

Hello, I am performing cor() of some of my data. For example, I'll do 3 corr() (many variables) operations, one for each of the three treatments. I then do the following: i <-lower.tri(treatment1.cor) cor(cbind(one = treatment1.corr[i], two = treatment2.corr[i], three = treatment3.corr[i])) Does this operation above tell me how correlated each of the three treatments is? Because this

It seems that the text command isn't working for a postscript device. Here is my code: #----------------------------- # I have stored some data in a 21 by 21 matrix called mat x <- 1:ncol(mat); y <- 1:nrow(mat); labs <- c(" ", "1298", "1537", "TP53", "786", "974", "1303", "1288", "1294",

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

Hello everybody, I have a question. I would like to get a correlation between constitutive and induced plant defence which I messured on 30 plant species. So I have table with Species, Induced defence (ID), and constitutive defence (CD). Since Induced and constitutive defence are not independant (so called spurious correlation) I should do a randomisation test. I have a syntax of my

Hi, Being a R-newbie I am wondering how to calculate a correlation coefficient (preferably with an associated p-value) for data like: > d[,1] [1] 25.5 25.3 25.1 NA 23.3 21.5 23.8 23.2 24.2 22.7 27.6 24.2 ... > d[,2] [1] 0.0 11.1 0.0 NA 0.0 10.1 10.6 9.5 0.0 57.9 0.0 0.0 ... Apparently corr(d) from the boot-library fails with NAs in the data, also cor.test cannot cope with a