correlation

How can rotation angle be determined by phase correlation(using fft) of 2 images? The algorithm given in http://en.wikipedia.org/wiki/Phase_correlation returns linear shift, not angular. It also mentions images have to be converted to log-polar coordinates to compute rotation. How is this conversion achieved in python? And post conversion do the same steps of the algorithm hold? S.Amar Nath Log polar transformation is actually rotation and scale invariant.. Rotation corresponds to shift in y axis and scaling corresponds to shift in x axis in log polar transformation So simple steps are as

Maybe I made a mistake. If so, I am sorry to ask this. I want to calculate Pearson's correlation coefficent by using scipy's pearsonr function. from scipy.stats.stats import pearsonr X = [4, 4, 4, 4, 4, 4] Y = [4, 5, 5, 4, 4, 4] pearsonr(X, Y) I get an error below RuntimeWarning: invalid value encountered in double_scalars ### The reason why I get an error is E[X] = 4 (Excepted Value of X is 4) I look at the code of pearsonr function in scpy.stats.stats.py. Some part of the pearsonr function is as follows. mx = x.mean() # which is 4 my = y.mean() # not necessary xm, ym = x-mx, y-my # xm = [0 0

I am plotting correlation plot with corrplot. I want to plot also the correlation coefficients: require(corrplot) test <- matrix(data = rnorm(400), nrow=20, ncol=20) corrplot(cor(test), method = "color", addCoef.col="grey", order = "AOE") But they are too big in the plot: Is there any way to make the font of the coefficent smaller? I've been looking at ?corrplot but there are only parameters to change the legend and axis font sizes ( cl.cex and tl.cex ). pch.cex doesn't work either. It is far from the answer, it is kind of a dirty hack, but this works (thanks user20650 for the idea): cex

I have the following codes to display a correlation matrix, panel.cor <- function(x, y, digits=2, prefix="", cex.cor) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r <- abs(cor(x, y)) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep="") if(missing(cex.cor)) cex <- 0.8/strwidth(txt) test <- cor.test(x,y) # borrowed from printCoefmat Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " ")) text(0.5, 0.5, txt, cex = cex * r) text(.8, .8, Signif, cex=cex, col=2) }

I am using R and have a question on correlations. A<-data.frame(A1=c(1,2,3,4,5),B1=c(6,7,8,9,10),C1=c(11,12,13,14,15 )) B<-data.frame(A2=c(6,7,7,10,11),B2=c(2,1,3,8,11),C2=c(1,5,16,7,8)) cor(A,B) # A2 B2 C2 # A1 0.9481224 0.9190183 0.459588 # B1 0.9481224 0.9190183 0.459588 # C1 0.9481224 0.9190183 0.459588 I wanted to obtain the p-value for each of the correlation coefficients in the matrix. Is this possible? I tried using rcorr function from Hmisc package but obtain only a single p-value and not for each correlation. A <- as.vector(t(A)) B <- as.vector(t(B)) rcorr(A, B) x y x 1.00 0.13 y 0

I am able to do this easily in Excel, but my dataset has gotten too large. In excel, I would use solver. Column A,B,C,D = random numbers Column E = random number (which I want to maximize the correlation to) Column F = A*x+B*y+C*z+D*j where x,y,z,j are coefficients resulted from solver In a separate cell, I would have correl(E,F) In solver, I would set the objective of correl(C,D) to max, by changing variables x,y and setting certain constraints: 1. A,B,C,D have to be between 0 and 1 2. A+B+C+D = 1 How can I do this in R? Thanks for the help. Since most optimization routines work best with no

I am newbie in R scripts :-) I need build a correlation matrix and I´am trying to configurate some parameters to adapt the graph. I am using the corrplot package. I Built a corrplot matrix this way: corrplot(cor(d1[,2:14], d1[,2:14]), method=c("color"), bg = "white", addgrid.col = "gray50", tl.cex=1, type="lower", tl.col = "black", col = colorRampPalette(c("red","white","blue"))(100)) I need show the values of correlation in the lower matrix inside the color matrix that I built. How i can do that? Is it possible exclude the main diagonal from the lower matrix? In this diagonl always we have