similar to: help: how to plot a circle on the scatter plot

R-Experts, Here below my R code working without error message but I don't get the results I am expecting. Here is the result I get: [1] "All values of t are equal to 0.28611928397257 \n Cannot calculate confidence intervals" NULL If someone knows how to solve my problem, really appreciate. Best, S ######################################################### # Difference in Spearman

I believe the problem is here: cor1 <- cor(x1, y1, method="spearman") cor2 <- cor(x2, y2, method="spearman") The x's and y's are not looked for in data (i.e. NSE) but in the environment where the function was defined, which is standard evaluation. Change the above to: cor1 <- with(d, cor(x1, y1, method="spearman")) cor2 <- with(d, cor(x2, y2,

Hi, what I would need are 2 vector pairs (x,y) and (x1,y1). x and x1 must have the same sd. y and y1 should also exhibit the same sd's but different ones as x and x1. Plotting x,y and x1,y1 should produce a plot with 2 vectors having a different slope. Plotting both vector pairs in one plot with fixed axes should reveal the different slope. many thanks syrvn -- View this message in

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben

Hi! All, I have 2 correlation matrices of 4000x4000 both with same row names and column names say cor1 and cor2. I have extracted some information from 1st matrix cor1 which is something like this: rowname colname cor1_value a b 0.8 b a 0.8 c f 0.62 d k 0.59 - - -- -

Hello all, I believe this can be done using bootstrap, but I am wondering if there is some other way that might be used to tackle this. #Let's say I have two pairs of samples: set.seed(100) s1 <- rnorm(100) s2 <- s1 + rnorm(100) x1 <- s1[1:99] y1 <- s2[1:99] x2 <- x1 y2 <- s2[2:100] #And both yield the following two correlations: cor(x1,y1) # 0.7568969 (cor1) cor(x2,y2)

Hello, In the Unix environment, I open a window by x11(). May I specify the position of this window by specifying the position of the top left of the window as in Windows environment? Or some other parameters can be used to do that? Thank you, Shengzhe

Hello I have a dataset named "b2" with 1521 rows, in that dataset i have 64 rows containing specific information. the rownumbers with specific info are: + i [1] 22 53 104 127 151 196 235 238 249 250 263 335 344 353 362 370 389 422 458 459 473 492 502 530 561 624 647 651 666 671 [31] 715 784 791 807 813 823 830 841 862 865 1036 1051 1062 1068

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick

Hello, When I open a new device by "windows()", how to hide the 3 buttons on the top-right corner of this window, since I want to make these buttons not work, and the window can be closed only by "dev.off()" or "graphics.off()". Thank you, Shengzhe

Hello, I have a data set with 15 variables (first one is the response) and 1200 observations. Now I use pls package to do the plsr as below. trainSet = as.data.frame(scale(trainSet, center = T, scale = T)) trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method = "kernelpls", model = TRUE, x = TRUE, y = TRUE) from the model, I wish to know the

# R 2.3.1 I read the data set into R, but the data set have two rows per record. i.e. ......... 105 1 1 1103 1 5 4 8 5 22 105 2 2 2 2 1 2 17 16 26 106 1 2 606 1 5 12 11 9 37 106 2 2 2 2 2 2 16 14 29

I'd like to get the sum of every other row in a data.frame. When I actually set about doing this, I get the error in the subject line of this message. A sample of my data is below, followed by the function call that should give me the results I want: > dput(head(sens2)) structure(list(Time = c(1328565067, 1328565067.05, 1328565067.1, 1328565067.15, 1328565067.2, 1328565067.25), Y =

Hello, I wish to plot some figures in a window in turn, but the size of these figures is different, so how can I change the size of the window by resetting the parameters before each plotting? Thank you, Shengzhe

Hello, When I use "windows()" to open several windows, e.g. 4 windows, device 2, device 3, device 4 and device 5. Normally using "dev.set()" to specify the current active window, if there is a way to specify the active window by mouse click? When I click device 3, device 3 becomes active (shown on device header), and when I click device 4, device 4 becomes active, so on....

Hi useRs, I'm trying for the first time to use a sem. The model finally runs, but gives a warning saying : "In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. " I found in R-help some posts on this warning, but my attemps to modify the code didn't change

Hi Rui, Many thanks. The R code works BUT the results I get are quite weird I guess ! MIC = 0.2650 Normal 95% CI = (0.9614, 1.0398) The MIC is not inside the confidence intervals ! Is there something wrong in the R code ? Here is the reproducible example : ########## C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2) D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9) library(minerva) mine(C,D)$MIC

Dear R-Experts, Here below is my R code (reproducible example) to calculate the confidence intervals around the spearman coefficient. ########## C=c(2,4,5,6,3,4,5,7,8,7,6,5,6,7,7,8,5,4,3,2) D=c(3,5,4,6,7,2,3,1,2,4,5,4,6,4,5,4,3,2,8,9) cor(C,D,method= "spearman") library(boot) myCor=function(data,index){ cor(data[index, ])[1,2] } results=boot(data=cbind(C,D),statistic=myCor, R=2000)

Hello, First of all, when I tried to use function mic I got an error. mic(cbind(C, D)) Error in mic(cbind(C, D)) : could not find function "mic" So I've changed your function myCor and all went well, with a warning relative to BCa intervals. myCor <- function(data, index){ mine(data[index, ])$MIC } results=boot(data = cbind(C,D), statistic = myCor, R = 2000)