similar to: number of observations used in cor when use="pairwise.obs"

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

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

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",

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,

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

Hi, I have just encountered a strange behaviour from 'cor' with regards to the treatment of NAs when calculating Spearman correlations. I guess it is a subtle bug. If I understand the help page correctly, the two modes 'complete.obs' and 'pairwise.complete.obs' specify how to deal with correlation coefficients when calculating a correlation _matrix_. When calculating

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Full_Name: Marek Ancukiewicz Version: 1.8.1 OS: Linux Submission from: (NULL) (132.183.12.87) Function cor() incorrectly handles missing observation with method="spearman": > x <- c(1,2,3,NA,5,6) > y <- c(4,NA,2,5,1,3) > cor(x,y,use="complete.obs",method="s") [1] -0.1428571 >

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 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

Dear All, I am not familiar with R yet I want to use it to perform some task, hence my posting here. I hope someone can help. I have a set of data, genes (rows) and samples (columns). I want to do a Pearson correlation on all the possible pairwise combinations of all the genes (2000). Does anyone have an idea of how to execute this in R? Thanks in advance. -- View this message in context:

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

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"))

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) +

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 all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights

Dear list, Following up on an earlier post, I would like to reorder a dataset and compute pairwise correlations. But I'm having some real problems getting this done. My data looks something like: Participant Stimulus Measurement p1 s`1 5 p1 s`2 6.1 p1 s`3 7 p2 s`1 4.8 p2