similar to: Incorrect handling of NA's in cor() (PR#6750)

Full_Name: Marek Ancukiewicz Version: 2.01 OS: Linux Submission from: (NULL) (132.183.12.87) It seems that the the standard error of prediction of the linear regression, caclulated with predict.lm is incorrect. Consider the following example where the standard error is first calculated with predict.lm, then using delta method. and finally, using the formula rms*sqrt(1+1/n+(xp-x0)^2/Sxx). Marek

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

Arrrrr, I think I've found a bug in the behavior of the stats::cor function when NAs are present, but in case I'm missing something, could you look over this example and let me know what you think: > a = c(1,3,NA,1,2) > b = c(1,2,1,1,4) > cor(a,b,method="spearman", use="complete.obs") [1] 0.8164966 > cor(a,b,method="spearman",

Full_Name: Marek Ancukiewicz Version: 0.64.0 OS: Linux (RedHat 6.0) Submission from: (NULL) (132.183.12.87) I've noticed that factor structures get recoded when the data is dumped using either dump or dput and then restored with source or dget. This occurs when the values taken by factors do not include 1. For example: a <-

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

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,

Pardon my ignorance, but is there a difference in cor.test between exact=FALSE and exact=NULL when method=spearman? Take for example: x<-c(1,2,2,3,4,5) y<-c(1,2,2,10,11,12) cor.test(x,y, method="spearman", exact=NULL) This gives an error message, Warning message: Cannot compute exact p-values with ties in: cor.test.default(x, y, method = "spearman", exact = NULL)

Hello to all of you, R-expeRts! I am trying to compute the cor.test for a matrix that i labelled mydata according to mydata=read.csv... then I converted my csv file into a matrix with the mydata=as.matrix(mydata) NOW, I need to get the p-values from the correlations... I can successfully get the spearman's correlation matrix with: cor(mydata, method="s",

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

hi, i am a new member. i am using R in finding correlation between two variables of unequal length. when i use cor(x,y,na.rm=T,use="complete") where x has observations from 1928 to 2006 & y has observations from 1950 to 2006. I used na.rm=T to use the "complete observations". So missing values should be handled by casewise deletion. But it gives me error Error in

i can see source code of function > cor function (x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman")) { na.method <- pmatch(use, c("all.obs", "complete.obs", "pairwise.complete.obs", "everything", "na.or.complete"))

Hi r lovers! I 'd like to apply the cor() function to a matrix which have some missing values As a matter of fact and quite logically indeed it doesn't work Is there a trick to replace the missing value by the mean of each variable or by any other relevant figures ? Or should I apply a special derivate of the cor() function, (I don't have any idea if it exists and have some trouble to

Dear R gurus, to compute the correlation matrix of "n" variables with "n_obs" observations each, possibly including NA, I use cor(M, use="pairwise.obs") where m is a "n" x "nobs" matrix. Now I want to know the number of observations actually used in this computation, namely for each pair of columns in M, say pair (i,j), I want to compute sum(

I can't hardly accept the result of cor function with pairwize.colplete.obs or complete.obs insert print statements in cor function, + if (method != "pearson") { + Rank <- function(u) if (is.matrix(u)) + apply(u, 2, rank, na.last = "keep") + else rank(u, na.last = "keep") + x <- Rank(x) +

With a data frame containing some X & Y variables I can get the between set correlations with cor(X,Y): > cor(NLSY[,1:2], NLSY[3:6]) antisoc hyperact income educ math 0.043381307 -0.07581733 0.25487753 0.2876875 read -0.003735785 -0.07555683 0.09114299 0.1884101 Is there somewhere an analog of pairs(X,Y) that will produce the pairwise plots of each X against each

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

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)

Friends I am new to R (and statistics) so am struggling a bit. Briefly... I am interested in getting the P-Value from cor(X) where X is a matrix. I have found cor.test. Verbosely... I have 4 vectors and can generate the corellation matrix... > cor(cbind(X1, X2, X3, X4)) X1 X2 X3 X4 X1 1.00000000 -0.06190365 -0.156972795 0.182547517 X2

Hi, I calculating the output of a function when applied to pairs of row from a single matrix or dataframe similar to how cor() and pairs() work. This is the code that I have been using: pairwise.apply <- function(x, FUN, ...){ n <- nrow(x) r <- rownames(x) output <- matrix(NA, nc=n, nr=n, dimnames=list(r, r)) for(i in 1:n){ for(j