correlation

问题 I'm trying to generate ordinal correlated variables with GenOrd package. However, there are some problems. I'm getting help on this page, which was written by Wesley https://www.r-bloggers.com/simulating-random-multivariate-correlated-data-categorical-variables/ In this page, and most of other explanation about GenOrd package, the marginal distirbution must be set. In this page, the marginal distribution is #The values are cumulative so for the first variable the first marginal will be .1,

问题 I want to calculate correlation statistics using cor.test(). I have a data matrix where the two pairs to be tested are on consecutive lines (I have more than thousand pairs so I need to correct for that also later). I was thinking that I could loop through every two and two lines in the matrix and perform the test (i.e. first test correlation between row1 and row2, then row3 and row4, row5 and row6 etc.), but I don't know how to make this kind of loop. This is how I do the test on a single

问题 I am somewhat new to programming and statistics, so please help me improve this question if it is formally not correct. I have a lot of parameters and a couple of result vectors I produced in a MonteCarlo simulation. Now I want to test the influence of each parameter for the result. I already got a script working with Kendall's Tau. Now I would like to compare with Spearman and Pearson rho. An example: from scipy.stats import spearmanr, kendalltau, pearsonr result = [106, 86, 100, 101, 99,

问题 These two lists contain data something like this: a = [1 2 1 3 1 2 1 1 1 2 1 1 2 1 4 1 ] b = [ 3480. 7080. 10440. 13200. 16800. 20400. 23880. 27480. 30840. 38040. 41520. 44880. 48480. 52080. 55680. 59280.] How to find correlation using python by importing rpy2 , I mean cor function. And the o/p has to lie between -1 and +1. 回答1: from rpy2.robjects.vectors import FloatVector from rpy2.robjects.packages import importr stats = importr('stats') a=[1, 2, 1, 3, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 4, 1 ]

问题 I am trying to split the columns of a dataframe to find pmcc of all possible combinations of (two)columns from a dataframe containing n columns, e.g. in this case, with 3 columns Length Diameter Height 0.455 0.365 0.095 0.350 0.265 0.090 0.530 0.420 0.135 0.440 0.365 0.125 0.330 0.255 0.22 here I have to find pmcc for all combinations, eg, (length, diameter), (diameter, height), etc. Any help! Thanks 回答1: data.frame(z = rnorm(100, 2), y = rnorm(100, 4), x = rnorm(100, 6)) -> frame combn

问题 The documentation of the numpy.correlate command says that the cross correlation of two arrays is computed as the general definition for signal processing in the way: z[k] = sum_n a[n] * conj(v[n+k]) This does not seem to be the case. It looks like the correlation is flipped. This would mean that either the sign in the last term of the formula is switched z[k] = sum_n a[n] * conj(v[n-k]) or that the two input vectors are in the wrong order. A simple implementation of the given formula would

问题 I have the following working-toy example: trunctiris <- iris [1:102,] analysis <- trunctiris %>% group_by(Species) %>% nest() %>% mutate(model = map(data, ~lm(Sepal.Length ~ Sepal.Width, data = .)), cor = map(data, ~tidy(cor.test(.x$Sepal.Length, .x$Sepal.Width), 3))) stats <- analysis %>% unnest(cor) ggplot(trunctiris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point(shape = 21) + geom_text(data = stats, aes(label = sprintf("r = %s", round(estimate, 3)), x = 7, y = 4)) + geom_text(data =

问题 sorry, I edited my question. In R, you can use the cor () function to find the correlation using only Pearson and Spearman correlation between Continuous variables. Which function should I use to get correlation between categorical variable and categorical variable? and Which function should I use to get correlation between categorical variables and Continuous variable Thank you in advance. 来源： https://stackoverflow.com/questions/41053431/how-to-find-the-correlation-between-continuous-and

问题 i've got in R a matrix of time series ( zoo object) which shows in each column historical prices for different equity ( within my universe); I have 20 columns ( 20 different stocks ). I transform this first matrix into a matrix of weekly return. My goal is to find a quick R code that help me to calcultate the mean of < the pairwise "rolling 52 weeks weekly return correlation">. It imply that every week, the code will compute one by one the 1y correlation of the stocks(1)'weekly return with

问题 I have two .csv files containing correlation matrices exported from R. One file contains the P-values and one contains the r-values. The row and column headers match exactly between the two files. I am trying to extract the r-values and corresponding row and column header for pairs only when the P-value < 0.05. Here is a sample of what the data in the r-value input file looks like (I have 1700+ correlated items, rather than only the two shown): Species1 Species2 Species1 1 0.9 Species2 0.9 1