correlation

In R there is a function ( cm.rnorm.cor , from package CreditMetrics ), that takes the amount of samples, the amount of variables, and a correlation matrix in order to create correlated data. Is there an equivalent in Python? numpy.random.multivariate_normal is the function that you want. Example: import numpy as np import matplotlib.pyplot as plt num_samples = 400 # The desired mean values of the sample. mu = np.array([5.0, 0.0, 10.0]) # The desired covariance matrix. r = np.array([ [ 3.40, -2.75, -2.00], [ -2.75, 5.50, 1.50], [ -2.00, 1.50, 1.25] ]) # Generate the random samples. y = np

I have huge matrix with a lot of missing values. I want to get the correlation between variables. 1. Is the solution cor(na.omit(matrix)) better than below? cor(matrix, use = "pairwise.complete.obs") I already have selected only variables having more than 20% of missing values. 2. Which is the best method to make sense ? I would vote for the second option. Sounds like you have a fair amount of missing data and so you would be looking for a sensible multiple imputation strategy to fill in the spaces. See Harrell's text "Regression Modeling Strategies" for a wealth of guidance on 'how's to do

I wonder how one can add another layer of important and needed complexity to a matrix correlation heatmap like for example the p value after the manner of the significance level stars in addition to the R2 value (-1 to 1)? It was NOT INTENDED in this question to put significance level stars OR the p values as text on each square of the matrix BUT rather to show this in a graphical out-of-the-box representation of significance level on each square of the matrix. I think only those who enjoy the blessing of INNOVATIVE thinking can win the applause to unravel this kind of solution in order to

Is there a better way to iterate through a set of parameters of a given dataset? Obviously, I try to get a table of correlation coefficients: columns are "CI, CVP, mean PAP, mean SAP", rows are "ALAT, ASAT, GGT, Bili, LDH, FBG". For each combination I´d like to get the correlation coefficient and the significance level (p=...). Below You see "the hard way". But is there a more elegant way, possibly with a printable table? attach(Liver) cor.test(CI, ALAT, method = "spearman") cor.test(CI, ASAT, method = "spearman") cor.test(CI, GGT, method = "spearman") cor.test(CI, Bili, method = "spearman")

I would like to calculate the correlation and the p value of that correlatio of each species (bac) to each of the factors (fac) in a second data frame. Both were measured at the same number of stations, but the number of bac and fac don't match. bac1 <- c(1,2,3,4,5) bac2 <- c(2,3,4,5,1) bac3 <- c(4,5,1,2,3) bac4 <- c(5,1,2,3,4) bac <- as.data.frame(cbind(bac1, bac2, bac3, bac4 )) colnames(bac) <- c("station1", "station2", "station3", "station4") rownames(bac) <- c("bac1", "bac2", "bac3", "bac4", "bac5") fac1 <- c(1,2,3,4,5,6) fac2 <- c(2,3,4,5,1,6) fac3<- c(3,4,5,1,2,6) fac4<- c(4,5,1,2,3, 6)