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corr

Builtin index · Statistics & ML

result = corr(x, y)

Input: x, y · Output: result

What it is

Pearson correlation between two length-N numeric vectors x and y.

Overloads

One call shape — this function is not overloaded.

CodeInputOutputDescription
result = corr(x, y)
x vectornot documented yet
y vectornot documented yet
result number
Returns one number in [-1, 1]. xcorr is the lag-by-lag version.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

  r = corr([1,2,3,4,5], [2,1,4,3,5])   # 0.8

from numerics-dsp

N = 200
Fs = 200
t = (0 to N - 1) / Fs
x = sin(2*pi*10*t)
shift = 15
y = zeros(N)
y[shift:N-1] = x[0:N-1-shift]

c = corr(x, x)
print("corr(x, x) = {c}")
c2 = corr(x, y)
print("corr(x, y) (shifted copy) = {c2:.4f}")

r = xcorr(x, y)
print("length(xcorr) = {length(r)} (expect 2N-1 = {2*N-1})")
zero_lag_idx = N - 1
best_idx = where r == max(r)
best_lag = best_idx[0] - zero_lag_idx
print("Best-match lag (samples): {best_lag} (true shift was {shift})")

from tour

a = randn(200, seed = 11)
b = 0.8 * a + 0.6 * randn(200, seed = 12)
print("corr(a, b): {round(corr(a, b), 3)}")
print("cov(a, b):  {round(cov(a, b), 3)}")
scatter(a, b, color = "#4169e1", alpha = 0.6)
title("corr = {round(corr(a, b), 3)}, cov = {round(cov(a, b), 3)}")
xlabel("a")
ylabel("b")
output
corr(a, b): 0.691
cov(a, b):  0.515
· scatter: figure 1 (200 points)
-2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 2.5 -2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0 2.5 3.0 x=-1.058, y=-0.826 x=0.594, y=0.636 x=0.88, y=1.305 x=-0.379, y=-0.489 x=0.152, y=1.215 x=1.407, y=0.754 x=0.231, y=-0.942 x=-1.291, y=-0.12 x=-0.085, y=-0.627 x=0.304, y=-0.101 x=0.037, y=0.004 x=-0.705, y=-0.429 x=0.241, y=-0.559 x=-0.582, y=-1.167 x=-0.277, y=-0.685 x=-1.636, y=-1.342 x=-0.865, y=-0.799 x=0.131, y=0.029 x=-1.581, y=-1.773 x=-2.048, y=-2.111 x=2.947, y=2.32 x=-0.456, y=-1.875 x=0.908, y=0.097 x=0.85, y=1.1 x=0.517, y=0.032 x=0.003, y=0.246 x=-0.201, y=0.212 x=0.466, y=-0.074 x=0.426, y=1.096 x=-0.066, y=-0.164 x=1.021, y=1.029 x=0.113, y=0.035 x=-0.998, y=-0.494 x=-0.066, y=-0.991 x=2.823, y=0.731 x=-0.046, y=-0.746 x=-0.029, y=-0.9 x=-0.031, y=0.196 x=-0.616, y=0.539 x=-1.349, y=-0.313 x=-1.085, y=-1.65 x=-1.452, y=-1.608 x=0.973, y=0.867 x=0.737, y=0.242 x=-0.185, y=0.468 x=1.175, y=1.485 x=-1.836, y=-0.822 x=-0.157, y=-0.076 x=0.407, y=0.307 x=-1.365, y=-1.022 x=-0.847, y=1.143 x=-0.762, y=0.021 x=-1.044, y=-0.505 x=0.478, y=1.123 x=0.316, y=-0.102 x=0.965, y=0.825 x=-0.751, y=-0.957 x=-0.988, y=-0.033 x=-1.22, y=-0.385 x=-0.681, y=-0.432 x=-0.621, y=0.256 x=0.918, y=0.744 x=1.182, y=-0.477 x=-0.039, y=0.425 x=0.585, y=-0.031 x=0.297, y=-0.535 x=0.112, y=-0.357 x=0.039, y=0.8 x=0.16, y=0.201 x=1.261, y=-0.955 x=0.95, y=0.238 x=-0.013, y=0.393 x=-1.577, y=-0.343 x=-0.67, y=-0.101 x=-0.631, y=-1.302 x=-0.84, y=-0.949 x=1.692, y=0.99 x=0.389, y=0.473 x=-0.261, y=-0.509 x=-0.491, y=-0.033 x=0.634, y=0.806 x=-0.342, y=0.622 x=-1.674, y=-1.63 x=0.039, y=-0.203 x=0.175, y=-0.688 x=0.206, y=0.293 x=1.405, y=0.994 x=-0.644, y=-0.672 x=-0.623, y=-0.7 x=1.569, y=0.384 x=-0.533, y=-0.154 x=0.682, y=-0.014 x=1.089, y=1.13 x=0.342, y=0.984 x=1.161, y=0.991 x=-0.232, y=0.289 x=-0.607, y=-0.737 x=0.295, y=0.07 x=0.275, y=0.25 x=-1.141, y=-0.456 x=0.461, y=0.554 x=-0.671, y=-0.906 x=0.513, y=1.154 x=0.499, y=-0.224 x=1.753, y=1.474 x=0.103, y=0.61 x=-0.351, y=0.258 x=0.553, y=-0.788 x=0.298, y=0.904 x=0.472, y=0.843 x=-0.94, y=-0.939 x=-1.062, y=-1.629 x=0.467, y=0.806 x=-0.653, y=-1.083 x=1.172, y=1.15 x=0.206, y=0.15 x=-1.077, y=-0.827 x=0.153, y=-0.987 x=0.202, y=0.439 x=0.011, y=-0.385 x=-0.67, y=0.029 x=1.367, y=0.502 x=0.955, y=0.531 x=0.352, y=0.238 x=1.543, y=0.703 x=-1.029, y=-0.474 x=0.104, y=0.508 x=0.877, y=-0.209 x=-1.366, y=-1.921 x=-1.583, y=-1.15 x=1.748, y=0.901 x=0.359, y=0.563 x=0.309, y=-0.002 x=1.322, y=1.929 x=-0.57, y=-0.79 x=-0.857, y=0.127 x=0.177, y=-0.37 x=-1.161, y=-1.563 x=0.371, y=1.281 x=-0.811, y=-1.39 x=0.168, y=1.089 x=-0.599, y=0.823 x=-0.452, y=-1.067 x=-1.241, y=-1.344 x=-0.711, y=-0.72 x=-0.233, y=-0.53 x=-0.344, y=0.761 x=1.152, y=0.27 x=0.989, y=0.896 x=0.308, y=0.67 x=-1.851, y=-1.723 x=0.866, y=0.926 x=-0.586, y=0.32 x=-0.204, y=-0.76 x=1.617, y=1.863 x=-0.897, y=-1.095 x=1.375, y=0.119 x=0.027, y=0.911 x=-0.268, y=-0.958 x=-0.58, y=-0.581 x=1.263, y=0.222 x=0.021, y=0.109 x=0.228, y=-0.664 x=0.002, y=-0.26 x=-0.301, y=0.237 x=0.646, y=0.863 x=0.272, y=-1.038 x=-0.432, y=-0.686 x=-0.123, y=1.22 x=0.844, y=1.163 x=-1.237, y=-1.157 x=2, y=1.132 x=-0.836, y=0.309 x=-0.332, y=-0.738 x=0.17, y=0.198 x=-0.327, y=0.587 x=-1.737, y=-1.131 x=-0.054, y=0.877 x=0.317, y=0.75 x=-1.406, y=-1.905 x=0.04, y=0.092 x=1.456, y=0.878 x=0.373, y=0.985 x=-0.912, y=-0.551 x=-0.104, y=0.042 x=0.349, y=0.549 x=1.098, y=0.24 x=0.588, y=-0.011 x=-0.164, y=-0.854 x=0.235, y=-0.679 x=-0.713, y=0.139 x=0.252, y=-0.404 x=-0.029, y=-0.281 x=-0.988, y=-0.818 x=0.223, y=-0.99 x=-0.591, y=0.839 x=-0.36, y=-0.088 x=-0.006, y=-0.156 x=-0.054, y=-0.158 x=0.644, y=-0.111 corr = 0.691, cov = 0.515 a b

from statistics-ml

How it works

corr(x, y) — Pearson correlation coefficient. Distinct from xcorr (a full lag-by-lag correlation *vector*): this is the single zero-lag, normalized-to-[-1,1] scalar summary.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.