correlation

I have a dataframe and would like to calculate the correlation (with Spearman, data is categorical and ranked) but only for a subset of columns. I tried with all, but R's cor () function only accepts numerical data (x must be numeric, says the error message), even if Spearman is used. One brute approach is to delete the non-numerical columns from the dataframe. This is not as elegant, for speed I still don't want to calculate correlations between all columns. I hope there is a way to simply say "calculate correlations for columns x, y, z". Column references could by number or by name. I

I am trying to find a way to generate correlated random numbers from several binomial distributions. I know how to do it with normal distributions (using mvrnorm), but I did not find a function applicable to binomial ones. You can generate correlated uniforms using the copula package, then use the qbinom function to convert those to binomial variables. Here is one quick example: library(copula) tmp <- normalCopula( 0.75, dim=2 ) x <- rcopula(tmp, 1000) x2 <- cbind( qbinom(x[,1], 10, 0.5), qbinom(x[,2], 15, 0.7) ) Now x2 is a matrix with the 2 columns representing 2 binomial variables that are

Having two arrays of double values, I want to compute correlation coefficient (single double value, just like the CORREL function in MS Excel). Is there some simple one-line solution in C#? I already discovered math lib called Meta Numerics. According to this SO question , it should do the job. Here is docs for Meta Numerics correlation method, which I don't get. Could pls somebody provide me with simple code snippet or example how to use the library? Note: At the end, I was forced to use one of custom implementations. But if someone reading this question knows good, well documented C# math

I have to compare two time-vs-voltage waveforms. Because of the peculiarity of the sources of these waveforms, one of them can be a time shifted version of the other. How can i find whether there is a time shift? and if yes, how much is it. I am doing this in Python and wish to use numpy/scipy libraries. Gus scipy provides a correlation function which will work fine for small input and also if you want non-circular correlation meaning that the signal will not wrap around. note that in mode='full' , the size of the array returned by signal.correlation is sum of the signal sizes minus one (i.e.