similar to: Creating a correlation Matrix

Hi, see ?cor in base package to get correlation matrix for your data. Maybe it could be usefull getting principal components (give a look to: ? princomp (base)) to reduce the number of variables. Hoping I helped you. Best regards, Vito You wrote: 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

I am trying to bring data into R from an excel spreadsheet in order to perform several statistical tests on it. I was trying to use odbcConnectExcel in the RODBC package. Once I am connected to the excel file, how do I select rows and columns from the file in order to analysis them in R.

Dear list I would like to compare two measurements of disease severity (M1 and M2), one of the is continuous (M1 ranging from 1 to 10) and the other is ordinal (M2 takes Low, Medium, high and very high). Do you think is ok to use cor() function to test whether the two agree, i.e correlate? I am afraid that if I set M2 to 1,2,3 and 4, the function cor() will take them as continuous and

dir1 <- list.files("C:\\Users\\aalyaari\\Desktop\\cor", "*.bin", full.names = TRUE) dir2 <- list.files("C:\\Users\\aalyaari\\Desktop\\cor2", "*.bin", full.names = TRUE) results <- list() for (.files in dir1){ # read in the 365 files as a vector of numbers for dir1 file1 <- do.call(rbind,(lapply(.files, readBin ,

Hi, This is probably trivial to most people out there, but I'm struggling with this. I have a data set which contains the closing prices (properly adjusted for dividends and splits) for several hundred securities and the closing prices for a general stock market index (S&P 500). I have no problem getting it into R with RODBC and manipulating it. There are no missing values. I can

Greeting. Dear Mr/Mrs/Miss, OTU ID Health Disease Bacterial 1 0.29 0.34 Bacterial 2 0.25 0.07 Bacterial 3 0.06 0.06 Bacterial 4 0.07 0.09 Bacterial 5 0.02 0.05 Above show the first 6 data sets, may I ask that the reason of R show the error like "Error in cor(data) : 'x' must be numeric" ? And how to solve it? Besides, isn't this data can conduct correlation matrix?

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix

Hello, R gives us the correlation functions cor(). (Many thanks ;-)) Does it also exist a "moving correlation" coefficient ? (like the moving average). If not, could someone give me some infos or link on how to practically implement such a function in R. (I did search for "moving correlation" on the R homepage but didn't find anything.) Thank you. Vincent

Sorry to bother everyone, but I've looked in all of the help files and manuals I have and I can't find the answer to this question. I'm doing principle component analysis by calculating the eigen vectors of a correlation matrix that I have that is composed of 21 parameters. I have the eigen vectors and their values that R produced for me but I'm not sure how to tell which

Hi Chin Yi, If you are trying to correlate "Health" with "Disease", i.e. cydf<-read.table(text="OTU ID Health Disease Bacterial 1 0.29 0.34 Bacterial 2 0.25 0.07 Bacterial 3 0.06 0.06 Bacterial 4 0.07 0.09 Bacterial 5 0.02 0.05", header=TRUE) print(cor(cydf$Health,cydf$Disease)) [1] 0.7103517 If you are getting that error, it probably means that

On 2017-07-05 11:56, Jim Lemon wrote: > Hi Chin Yi, > If you are trying to correlate "Health" with "Disease", i.e. > > cydf<-read.table(text="OTU ID Health Disease > Bacterial 1 0.29 0.34 > Bacterial 2 0.25 0.07 > Bacterial 3 0.06 0.06 > Bacterial 4 0.07 0.09 > Bacterial 5 0.02 0.05", > header=TRUE) >

Hello, I am new to R and have a problem I have had some trouble with. Basically I have a list of some 80000 genes or so with data points for expression levels at various time points/conditions. I also have subsets of these, usually only a few hundred genes in size, known to be associated with some biological process. What I want to do is correlate the entire list with my subset and then

Hello, I am quite new to R and I am trying to get a round correlation from a table with dozens of columns. However, all the columns contain several blank places which show to me as NAs. Then, when I type round(cor(data),2), I get no results - everything (except correlation of one column with the same one, of course) is NA. I do not want to replace NA with zero, because it would ruin the results. I

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:

Hi all-- A quick question about the dr () function. I am using this function to reduce the dimensions of a data set I have that involves 14 predictor variables and one predictant or response. The goal is to discover which variables play the most important role in determining the response and, thus, to reduce the variables. I would like to use the sliced inverse regression method (SIR) within

So I am very new to R. Have been using python for a project and need to calculate the correlation coefficient matrix for my data set. the data is in the range of 10-15 observations of 230,000 variables. ie the correlation matrix would be 230,000X230,000 Using python and the numpy.corrcoef() I run out of memory if I try to do this with more than ~30,000 variables. I was able to load the data into

Hi, R! Question1: I am trying to correlate two vectors of numbers (two columns of microarray signal values) by using the non-parametric Spearman's rank correlation coefficient rho: > cor.test(V2.Signal,V3.Signal,method="spearman") but I get the error message: Error in if (q > (n^3 - n)/6) pspearman(q - 1, n, lower.tail = FALSE) else pspearman(q, : missing value

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

Dear everyone, I am new in R and I've got difficulties in realizing the following tasks: -I have variables (factors) with different numbers of levels, either 1, 2 or 3. -I have a matrix containing these 204 factors and I have to correlate them by groups of 4 variables. -I have to delete the factors just having one level ( because when correlating one-level factors, the output is NA) here