similar to: Null values In R.

Hello R-Community, I am a rookie in R and I am fascinated with the power of bio computing by R. I am analysing gene expression data from Real time PCR. I have used absolute gene quantitation to measure gene copy number in all my transcripts. All my data has been normalised them to a housekeeping gene, which is constitutive expressed. My problem is as follows. After normalising some of the genes

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

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

Dear R I'm trying to do a correlation matrix for some variables I have. Unfortunately there are some NA entries for some of the variables I tried the following cor(sleep[c("logbw", "logbrw", "SlowSleep", "ParaSleep", "loglife", "loggest")]) but it told me Error in cor(sleep[c("logbw", "logbrw",

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",

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) +

(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 <-

Hi, I have recently been using the R program and encountered a recurring problem. I have been trying calculate the correlation of a 16 column table. Everytime I type in cor(test), where test is data that I uploaded into R using the read.table function, I get an error: Error in cor(test) : missing observations in cov/cor In addition: Warning message: In cor(test) : NAs introduced by coercion

Is there an R function for computing a variance-covariance matrix that guarantees that it will have no negative eigenvalues? In my case, there is a *lot* of missing data, especially for a subset of variables. I think my tactic will be to compute cor(x, use="pairwise.complete.obs") and then pre- and post-multiply by a diagonal matrix of standard deviations that were computed based

Dear R subscribees, First, I am new to R and I apologize if the question is naive. I am trying to find out where arguments specifications can be found. For example, the code below (at the very bottom of this email) contains the argument 'digits' in the function 'lin' which printout my output to the 3rd decimal place. Somebody showed me the 'digits' argument, but did

Hello! I am running principle components analysis using princomp function in pacakge psych. mypc <- princomp(mydataforpc, cor=TRUE) Question: I'd like to use pairwise deletion of missing cases when correlations are calculated. I.e., I'd like to have a correlation between any 2 variables to be based on all cases that have valid values on both variables. What should my na.action be in

Greetings R users, I am an R graphics newbie trying to produce a custom trellis plot using pairs() with R 2.7.2. I have spatial data on which I run a geographically weighted regression (gwr, using the -spgwr- package). I want to check the gwr coefficients for multicollinearity and spatial association, following Wheeler and Tiefelsdorf (2005), and I would like to summarize the results of this

Dear all, Does this make any sense: var() = cov() != acf(lag.max=0, type="covariance")? I have daily data of IBM for May 2005, and I'm using the logarithmic return: > ibm200505$LRAdj.Close [1] NA 0.0203152 0.0005508 -0.0148397 -0.0025182 0.0092025 -0.0013889 [8] 0.0098196 -0.0103757 -0.0274917 0.0005716 -0.0159842 -0.0074306 0.0091710 [15] 0.0002898 0.0226306

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",

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

Hello all, I just upgraded to R 2.7.0 and found that the behavior of 'var' and 'sd' have changed in the presence NAs (this wasn't explicit in the NEWS file, though I see it probably has to do with the change for cor/cov). Anyway, I just want to make sure that it was intentional to produce an error when there was all NAs and na.rm=TRUE, rather than returning an NA (like R

Hi, I have a problem to define the range of x and y axis in pairs() for my scatterplots. In low-level plots I can specify that by providing xlim and ylim. This also works for pairs() even if warnings tell me that it doesn't (see below). But if I add upper.panel and/or lower.panel it doesn't work - I get an error message saying that there's an error in "upper.panel

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. 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