similar to: Error in cor.default(x1, x2) : missing observations in cov/cor

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

Can I suggest that in cov and cor the lines if (is.data.frame(x)) x <- as.matrix(x) if (is.data.frame(y)) y <- as.matrix(y) be replaced by if (is.data.frame(x)) x <- data.matrix(x) # coerce logical and factors if (is.data.frame(y)) y <- data.matrix(y) # likewise This would allow things like > data(iris) > cor(iris) which

I recently started using R 2.7.2, and noticed a surprising change in the behavior of var() on NA data: R 2.6.1 (Patched), 2007-11-26, svn.rev 43541, x86_64-unknown-linux-gnu: > stdev(rep(NA,3), na.rm=F) [1] NA > stdev(rep(NA,3), na.rm=T) [1] NA > var(rep(NA,3), na.rm=T, use="complete.obs") [1] NA R 2.7.2 (Patched), 2008-09-02, svn.rev 46491,

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

(all in subject). If I want to do a PC analysis in a situation with missing data, I may want to have same flexibility as with "cor(.)", e.g., I may want princomp(x, ..., use.obs = "pairwise.complete") Actually, I may want even more flexibility. Currently, princomp(.) has if (cor) cv <- get("cor", envir = .GlobalEnv)(z) else cv <-

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

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:

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) +

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

Hello, I have a big dataframe with *NO* na's (9 columns, 293380 rows). # doing memory.limit(size = 1000000000) cor(x) #gives Error in cor(x) : missing observations in cov/cor In addition: Warning message: NAs introduced by coercion #I found the obvious workaround: COR <- matrix(rep(0, 81),9,9) for (i in 1:9) for (j in 1:9) {if (i>j) COR[i,j] <- cor (x[,i],x[,j])} #which works fine,

Hi, I used to get a really useful graph when I ran the following command using the MASS library: > cov.rob(cbind(dekeyser$AGE,dekeyser$GJTSCORE),cor=T) Besides the regular output, a graph appeared that had the classical correlation and the robust correlation, and two ellipses, one surrounding the data that would be used in the classical correlation and the other surrounding the data in the

Full_Name: Ju-Sung Lee Version: 1.9.1 OS: Windows XP Submission from: (NULL) (128.2.79.102) The following commands causes R to crash: a = array(1:3) dimnames(a)[[1]] = c('1','2','3') cor(a,matrix(1:3)) not naming the array seems to work.

Is there a simple way to run cor.test on for a matrix of correlations? Of course, cor on a data frame produces a correlation matrix, but cor.test will only take two variables at a time. Is there a way to get behavior similar to that of cor with cor.test? I suppose the programming alternative would be to run two for loops with the number of items and cor test embedded accessing the columns of

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",

Hi, Is there a limit on the number of observations for using cor.test. For example, > library(ctest) > cor.test(rnorm(3000), rnorm(3000), method="spearman") Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value where logical needed In addition: Warning message: NAs introduced by coercion I mainly want to calculate

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(

Hi everyone. I am having problems with NULL values. I understand in R one can command the program to skip null values. Can some one help me on the command line for that. Do i enter is as part of the string in: a<- read.table("filename.csv", header = T, row.names=1, sep=","); My problem is largely when i attempt to use correlation for my data... xcc <- cor(a); The

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

Hello, I think this qualify as a bug > x<-c(1,2,3,4,NA,6,7) > mean(x) [1] NA > mean(x,na.rm=T) [1] 3.833333 > sd(x) Error in var(as.vector(x)) : missing observations in cov/cor > sd(x,na.rm=T) Error in sd(x, na.rm = T) : unused argument(s) (na.rm ...) > var(x) Error in var(x) : missing observations in cov/cor > var(x,na.rm=T) [1] 5.366667 > why sd() does not

Dear All, I have a file (a matrix) on which I have to compute the correlations. The function "cov" compute the correlations between the columns of a matrix, but I want to compute the correlations between the raws. Can somebody say to me how I can carry out that? Thanks Laurence -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list --