correlation

问题 I have recently studied the concepts of CCA and wanted to implement it in MATLAB. However there is an existing matlab command canoncorr present. I wanted to write my own code. I have studied it extensively and found three approaches : 1: Hardoon : The approach uses lagrange multipliers to decompose the problem into an generalised eigenvalue problem. The code can be found here : cca_hardoon For sanity sake I am also giving the code here : The data has to be centered previously. function [Wx,

问题 I am trying to track an object using correlation. I am finding a smaller patch in a larger image, frame by frame. For this, I am finding the shift in the patch, and where correlation is maximum, update the patch with a new patch. My code is: cv::Mat im_float_2,imagePart_out; cv::Mat im_floatBig; cv::Scalar im1_Mean, im1_Std, im2_Mean, im2_Std; double covar, correl; int n_pixels; void computeShift() { int maxRow=0, maxCol=0, TX, TY; double GMAX=0; Mat image_window = Mat::zeros(imagePart.rows,

问题 Why is there no tutorial online about replicating R's basic cor() function in tableau. All I can find are tutorials to visualize correlations of values by dimension. I just want to see how all variables in my data are correlated to each other. And thanks for the support @user1036719 回答1: All, I have figured out the simplest way to solve my own problem. With the introduction of Tableau 9.0, you can load .RData files directly into Tableau. Here is how you go about it: 1) Simply save() your

问题 If we have the data set: import pandas as pd a = pd.DataFrame({"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]}) b = pd.DataFrame({"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]}) How does one create a correlation matrix, in which the y-axis represents "a" and the x-axis represents "b"? The aim is to see correlations between the matching columns of the two datasets like this:

问题 I have a stationary time series to which I want to fit a linear model with an autoregressive term to correct for serial correlation, i.e. using the formula At = c1*Bt + c2*Ct + ut, where ut = r*ut-1 + et (ut is an AR(1) term to correct for serial correlation in the error terms) Does anyone know what to use in R to model this? Thanks Karl 回答1: The GLMMarp package will fit these models. If you just want a linear model with Gaussian errors, you can do it with the arima() function where the

问题 I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is: degrees of freedom according to Chelton(1983) and I can't find a proper way to calculate the normalized cross correlation function using np.correlate, I always get an output that it isn't in between -1, 1. Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?

问题 I would like to return the significant matches from the following result shown in Fig. 1 library("corrplot") M <- cor(mtcars) # http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram cor.mtest <- function(mat, ...) { mat <- as.matrix(mat) n <- ncol(mat) p.mat<- matrix(NA, n, n) diag(p.mat) <- 0 for (i in 1:(n - 1)) { for (j in (i + 1):n) { tmp <- cor.test(mat[, i], mat[, j], ...) p.mat[i, j] <- p.mat[j, i] <- tmp$p.value } } colnames(p.mat) <- rownames(p.mat) <-

问题 I am currently designing a correlation engine in java which is extracting data from pdf files and correlating (raising alerts where necessary) it structured data from a relational database. Focusing on the processing of the pdf files the system consists of: A component which is performing the custom extraction from the pdf. A component which parses the sometimes unordered unclean data into the required data structures A normalisation component which will normalises the values for comparison

问题 I am trying to generate a table output of a correlation matrix. Specifically, I am using a for loop in order to identify a correlation between all data in columns 4:40 to column 1. While the results of the table are decent, it does not identify what is being compared to what. In checking attributes of cor.test ,I find that data.name is being given as x[1] and y[1] which is not good enough to trace back which columns is being compared to what. Here is my code: input <- read.delim(file=

问题 I want to create a network from a correlation matrix and plot it. I'm trying to use igraph for this. This is a subset of my data. mydata Taxon CD1 CD2 Actinomycetaceae;g__Actinomyces 0.072998825 0.031399459 Coriobacteriaceae;g__Atopobium 0.040946468 0.002703265 Corynebacteriaceae;g__Corynebacterium 0.002517201 0.006446247 Micrococcaceae;g__Rothia 0.001174694 0.002703265 Porphyromonadaceae;g__Porphyromonas 0.023326061 0.114368892 Prevotellaceae;g__Prevotella 0.252894781 0.102308172