similar to: Excluding NAs from round correlation

Hello, I am trying to get a linear model of y ~ log(x). *> lm (y~log(x))* However, I always get an error report: /Error in model.frame.default(formula = y ~ log(x), drop.unused.levels = TRUE) : variable lengths differ (found for 'log(x)')/ *Here was my y:* > y [1] 0.4500000 0.0500000 0.5000000 0.4000000 0.0000000 0.5000000 0.4000000 [8] 0.0500000

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 --

Hi all, I have a binary matrix with NAs included. Each row and column includes at least one NA, so I don't want to omit them. Is there a way to sum across rows and columns, ignoring the NAs but not deleting the row or column? If not, I suppose I can write a loop function, but I have learned that it is best to stay away from loops if possible. Thanks for any help, Wade

Hi all: I have a question on how to go about creating a correlation matrix. I have a huge amount of data....21 variables for 3471 times. I want to see how each of the variables correlate to each other. Any help would be appreciated, including which package and which functions I should use to do this. Thanks, Jessica Higgs Masters Student Department of Meteorology Penn State University

Hi everyone, I'm having trouble applying the Cor() function to two matrices, both of which contain NAs. I am doing the following: a<-cor(m1, m2, use="complete.obs") ... and I get the following error message: Error in cor(m1, m2, use = "complete.obs") : no complete element pairs Does anyone know how I can apply a correlation, ignoring any NAs? Thanks, rcoder --

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",

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

hello, after an anova I use pairwise.t.test(), it gives only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function

[Moderator: This was erronously sent to R-announce, and filtered fortunately ] Hi, This is my first time to use R. I'm wondering which function I can use to do multiple comparison. I have an lm object and want to do multiple comparison based on this object. Splus has a function multicomp() but I could not find a similar one in R. Thanks in advance. Really appreciate it. Julie

Hello. I would like to estimate the correlation coefficient from two samples with Bootstrapping using the R-function sample(). The problem is, that I have to sample pairwise. For example if I have got two time series and I draw from the first series the value from 1912 I need the value from 1912 from the second sample, too. Example: Imagine that a and b are two time series with returns for

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 R users, I wanted to calculated a sample Variance covariance matrix of a five-variate normal distribution. However I stuck to calculate each element of that matrix. My question is should I calculate ordinary variance and covariances, taking pairwise variables? or I should take partial covariance between any two variables, keeping other fixed. In my decent opinion is I should go for the

Dear R-users, I have been using R and its core-packages with great satisfaction now for many years, and have recently started using the "ccf" function (part of the "stats" package version 2.16.0), about which I have a question. The "ccf"-algorithm for calculating the cross-correlation between two time series always calculates the mean and standard deviation per time

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:

Dear R community: I want to simulate a regression matrix which is generated from an orthonormal matrix X of dimension 30*10 with different between-column pairwise correlation coefficients generated from uniform distribution U(-1,1). Thanks in advance! Rui [[alternative HTML version deleted]]

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

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

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

Hi everybody. I'm trying to do a correlation matrix in a list of files. Each file contains 2 columns: "capt1" and "capt2". For the example, I merged all in one data.frame. My data also contains many missing data. The aim is to do a correlation matrix for the same data for course (one correlation matrix for capt1 and another for capt2). For the moment, I have a correlation

Hello All, I have a panel date - here a small-scale example: df <- data.frame(cbind(rep(c("AUT","BEL","DEN","GER"),4),cbind(rep(c(1999,2000,2001,2002),4)),sample(10,16,replace=T))) names(df) <- c("country","year","x") SORT <- c("GER","BEL","DEN","AUT") I need to compute the