search for: corring

Hello, I'm trying to do cor(x1,x2) and I get the following error: Error in cor.default(x1, x2) : missing observations in cov/cor A few things: 1. I've used cor() many times and have never encountered this error. 2. length(x1) = length(x2) 3. is.numeric(x1) = is.numeric(x2) = TRUE 4. which(is.na(x1)) = which(is.na(x2)) = integer(0) {the same goes for is.nan()} 5. I also try

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Hi, I am trying to use R for some survey analysis, and need to compute the significance of some correlations. I read the man pages for cor and cor.test, but I am confused about - whether these functions are intended to work the same way - about how these functions handle NA values - whether cor.test supports 'use = complete.obs'. Some example output may explain why I am confused:

I' ve seen that several people are looking for a function that creates a barplot with an error indicators (I was one of them myself). Maybe you will find the following code helpful (There are some examples how to use it at the end): # Creates a barplot. #bar.plot() needs a datavector for the height of bars and a error #indicator for the interval #many of the usual R parameters can be set:

Hi, Following the Writing R Extensions manual, I created a method for the cor function. As cor is not a generic, I followed the advice of section 6.1 of the same manual and did the following: cor <- function(x, ...) UseMethod("cor") cor.default <- stats::cor cor.symcoca <- function{ some code } I used package.skeleton to create the basic set-up of my package, containing the

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)

Hello, I am trying to make a correlation matrix in R using cor() and also to get the p-value for each correlation using cor.test(). I can't get these commands to work. I'm getting errors like the following: cor(Pollution, Wet.days) Error in inherits(x, "data.frame") : Object "Wet.days" not found cor("Pollution", "Wet.days") Error in

Full_Name: Clay B Version: 2.5.0 (2007-04-23) OS: Solaris Nevada Build 55b Submission from: (NULL) (65.101.229.198) I find that running this script causes R to segfault reliably. However, running just for one system at a time (modifying the for loop updating iter to run just for a system at a time works). The system is a Sun W2100z with 12 GB of ram, and R segfaults using only around 360 MB of

In using cor(data.frame), it is annoying that you have to explicitly filter out non-numeric columns, and when you don't, the error message is misleading: > cor(iris) Error in cor(iris) : missing observations in cov/cor In addition: Warning message: In cor(iris) : NAs introduced by coercion It would be nicer if stats:::cor() did the equivalent *itself* of the following for a data.frame:

Odd behaviour of function cor() in R-2.10.1-64bit-Unix In a dataset with 1366 patients and 244 clinical variables Spearman's Rho was calculated for some fatty acids and BMI and came over something rather odd: R seems to calculate Rho differently on 2.10.1-64bit-Unix and 2.9.0-32bit-Windows when I calculate the complete (244x244) correlation matrix and then pick out the values I am

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Full_Name: Marek Ancukiewicz Version: 2.10.1 OS: Linux Submission from: (NULL) (74.0.49.2) Both cor() and cor.test() incorrectly handle ordered variables with method="kendall", cor() incorrectly handles ordered variables for method="spearman" (method="person" always works correctly, while method="spearman" works for cor.test, but not for cor()). In

Hallo! Is there an error in cor to calculate Spearman correlation with cor if there are NA's? cor.test gives the correct result. At least there is a difference. Or am I doing something wrong??? Does anybody know something about this? a<-c(2,4,3,NA) b<-c(4,1,2,3) cor(a, b, method="spearman", use="complete.obs") # -0.9819805 cor.test(a, b,

Dear R maintainers, R is great. Now that I have that out of the way, I believe I have encountered a bug, or at least an inconsistency, in how Spearman and Kendall rank correlations are handled. Specifically, cor() and cor.test() do not produce the same answer when the data contain NAs. cor() treats the NAs as data, while cor.test() eliminates them. The option use="complete.obs" has

Full_Name: Paul Bailey Version: 2.1.1 OS: OS X 10.3 Submission from: (NULL) (68.252.250.144) ?cor [tells me that it has a na.rm variable] > cor(frame2[1,],frame2[2,],na.rm=T) Error in cor(frame2[1, ], frame2[2, ], na.rm = T) : unused argument(s) (na.rm ...) hmm.

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

my programe is below: a=c(1,2,1,1,1); b=c(1,2,3,4,1); c=c(3,4,3,3,3); d=c(1,2,3,5,1); e=c(1,5,3,5,1) data.f=data.frame(a,b,c,d,e) origin.data<-data.f cor.matrix<-cor(origin.data) origin.cor<-cor.matrix m<-0 for(i in 1:(cor.matrix[1]-1)) { for(j in (i+1):(cor.matrix[2])) { if (cor.matrix[i,j]>=0.95) { data.f<-data.f[,-i]; i<-i+1 }

Hi R users, I generated a square correlation matrix for the dat dataframe below; dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0), g2=c(0,1,0,1,0,1,1,0,0), g3=c(1,1,0,0,0,1,0,0,0), g4=c(0,1,0,1,1,1,1,1,0)) library("Hmisc") dat.rcorr = rcorr(as.matrix(dat)) dat.r <-round(dat.rcorr$r,2) however, I want to modify this correlation calculation;

Hi there, I got a piece of code for the Iris data which allows to display correlation coefficients for each Iris species in the lower panel (color coded). I would now like to add e.g. a "*" to show the significance of each correlation next to the correlation coefficient. Furthermore I would like to make a t.test between the species "setosa" and "versicolor" for

Using Windows System, R 2.1.0 d is a data frame, 48 rows, 10 columns cor(d) works properly providing all pairwise Pearson correlation coefficients among columns cor.test(d) gives error message "Error in cor.test.default(d) : argument "y" is missing, with no default" Why? Thanks, MCG