correlation

I have some code in a Shiny app that produces the first plot below. As you can see the font size varies with the size of the correlation coefficient. I would like to produce something similar with ggpairs (GGally) or ggplot2. The second image below was produced with the following code: library(GGally) ggpairs(df, upper = list(params = c(size = 10)), lower = list(continuous = "smooth", params = c(method = "loess", fill = "blue")) ) As you can see the size of the correlation font is adjustable using size but when I set a vector of sizes only the first value is used. I would also like to remove

I've read some explanations of how autocorrelation can be more efficiently calculated using the fft of a signal, multiplying the real part by the complex conjugate (Fourier domain), then using the inverse fft, but I'm having trouble realizing this in Matlab because at a detailed level. Amro Just like you stated, take the fft and multiply pointwise by its complex conjugate, then use the inverse fft (or in the case of cross-correlation of two signals: Corr(x,y) <=> FFT(x)FFT(y)* ) x = rand(100,1); len = length(x); %# autocorrelation nfft = 2^nextpow2(2*len-1); r = ifft( fft(x,nfft) .* conj(fft(x

Cor.test() takes vectors x and y as arguments, but I have an entire matrix of data that I want to test, pairwise. Cor() takes this matrix as an argument just fine, and I'm hoping to find a way to do the same for cor.test() . The common advice from other folks seems to be to use cor.prob() : https://stat.ethz.ch/pipermail/r-help/2001-November/016201.html But these p-values are not the same as those generated by cor.test() !!! Cor.test() also seems better equipped to handle pairwise deletion (I have quite a bit of missing data in my data set) than cor.prob() . Does anybody have any alternatives

I have the following pandas dataframe Top15 : I create a column that estimates the number of citable documents per person: Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita'] Top15['Citable docs per Capita'] = Top15['Citable documents'] / Top15['PopEst'] I want to know the correlation between the number of citable documents per capita and the energy supply per capita. So I use the .corr() method (Pearson's correlation): data = Top15[['Citable docs per Capita','Energy Supply per Capita']] correlation = data.corr(method='pearson') I want to return a single number, but

I have 92 set of data of same type. I want to make a correlation matrix for any two combination possible. i.e. I want a matrix of 92 x92. such that element (ci,cj) should be correlation between ci and cj. How do I do that? Manuel Ramón An example, d &lt- data.frame(x1=rnorm(10), x2=rnorm(10), x3=rnorm(10)) cor(d) # get correlations (returns matrix) You could use 'corrplot' package. d <- data.frame(x1=rnorm(10), x2=rnorm(10), x3=rnorm(10)) M <- cor(d) # get correlations library('corrplot') #package corrplot corrplot(M, method = "circle") #plot matrix More information here: http://cran.r-project