correlation

I am interested in computing the power spectrum of a system of particles (~100,000) in 3D space with Python. What I have found so far is a group of functions in Numpy ( fft , fftn ,..) which compute the discrete Fourier transform, of which the square of the absolute value is the power spectrum. My question is a matter of how my data are being represented - and truthfully may be fairly simple to answer. The data structure I have is an array which has a shape of ( n ,2), n being the number of particles I have, and each column representing either the x, y, and z coordinate of the n particles. The

all. I want to obtain the p-value from a correlation matrix using dplyr and/or broom packages and testing multiple variables at the same time . I'm aware of other methods, but dplyr seems easier and more intuitive for me. In addition, dplyr will need to correlate each variable to obtain the specific p-value, what makes the process easier and faster. I checked other links, but they did not work for this question ( example 1 , example 2 , example 3 ) When I use this code, the correlation coefficients are reported. However, the P-values are not. agreg_base_tipo_a %>% dplyr::select(S2.RT, BIS

I am using corrplot to create a correlation heatmap, but I don't like the default legend - it is too big. So I was trying to use the colorlegend() to add the legend after I create the plot (and disable the default legend with cl.pos="n" ). Only problem is that I can't figure out how to change the position of the legend - it ends up on the lower left. Ideally, I could place it on the top right, but I looked through the options for colorlegend and plot and can't figure this out. For example: # load libraries and create color scale library(corrplot) library(RColorBrewer) scalebluered <-

set.seed(8) df <- data.frame( A=sample(c(1:3), 10, replace=T), B=sample(c(1:3), 10, replace=T), C=sample(c(1:3), 10, replace=T), D=sample(c(1:3), 10, replace=T), E=sample(c(1:3), 10, replace=T), F=sample(c(1:3), 10, replace=T)) Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here , here and here , but nevertheless failed to produce an acceptable code. For instance, I tried this: require(plyr) require(dplyr) df %>% rowwise() %>% mutate(c

I have just started working using CCA in Matlab. I have two vectors X and Y of dimension 60x1920 and 60x1536 with the number of samples being 60 and variables in the different set of vectors being 1920 and 1536 respectively. I want to know do CCA for reducing them to the subspace and then do feature matching. I am using this commands. %% DO CCA [A,B,r,U,V] = canoncorr(X,Y); The output I get is this : Name Size Bytes Class Attributes A 1920x58 890880 double B 1536x58 712704 double U 60x58 27840 double V 60x58 27840 double r 1x58 464 double Can anyone please tell me what these variables mean. I

I am studying the correlation between a set of input variables and a response variable, price. These are all in time series. 1) Is it necessary that I smooth out the curve where the input variable is cyclical (autoregressive)? If so, how? 2) Once a correlation is established, I would like to quantify exactly how the input variable affects the response variable. Eg: "Once X increases >10% then there is an 2% increase in y 6 months later." Which python libraries should I be looking at to implement this - in particular to figure out the lag time between two correlated occurrences ? Example: I

I'm trying to convert this example of Bayesian correlation for PyMC2 to PyMC3, but get completely different results. Most importantly, the mean of the multivariate Normal distribution quickly goes to zero, whereas it should be around 400 (as it is for PyMC2). Consequently, the estimated correlation quickly goes towards 1, which is wrong as well. The full code is available in this notebook for PyMC2 and in this notebook for PyMC3 . The relevant code for PyMC2 is def analyze(data): # priors might be adapted here to be less flat mu = pymc.Normal('mu', 0, 0.000001, size=2) sigma = pymc.Uniform(