Encontrar correlaciones en datos no lineales

Desde una perspectiva de señalización, el mundo es un lugar ruidoso. Para darle sentido a algo, tenemos que ser selectivos con nuestra atención.

Los humanos, en el transcurso de millones de años de selección natural, nos hemos vuelto bastante buenos para filtrar las señales de fondo. Aprendemos a asociar señales particulares con ciertos eventos.

Por ejemplo, imagine que está jugando al tenis de mesa en una oficina ajetreada.

Para devolver el tiro de su oponente, debe realizar una gran variedad de cálculos y juicios complejos, teniendo en cuenta múltiples señales sensoriales en competencia.

Para predecir el movimiento de la pelota, su cerebro tiene que muestrear repetidamente la posición actual de la pelota y estimar su trayectoria futura. Los jugadores más avanzados también tendrán en cuenta cualquier giro que su oponente haya aplicado al tiro.

Finalmente, para jugar tu propio tiro, debes tener en cuenta la posición de tu oponente, tu propia posición, la velocidad de la pelota y cualquier giro que pretendas aplicar.

Todo esto implica una cantidad asombrosa de cálculo diferencial subconsciente. Damos por sentado que, en términos generales, nuestro sistema nervioso puede hacer esto automáticamente (al menos después de un poco de práctica).

Igual de impresionante es cómo el cerebro humano asigna importancia diferencial a cada una de las innumerables señales competitivas que recibe. La posición de la pelota, por ejemplo, se considera más relevante que, digamos, la conversación que tiene lugar detrás de usted o la puerta que se abre frente a usted.

Esto puede parecer tan obvio que parece indigno de decirlo, pero eso es testimonio de lo buenos que somos para aprender a hacer predicciones precisas a partir de datos ruidosos.

Ciertamente, una máquina de estado en blanco con un flujo continuo de datos audiovisuales enfrentaría una tarea difícil al saber qué señales predicen mejor el curso de acción óptimo.

Afortunadamente, existen métodos estadísticos y computacionales que se pueden utilizar para identificar patrones en datos complejos y ruidosos.

Correlación 101

En términos generales, cuando hablamos de "correlación" entre dos variables, nos estamos refiriendo a su "relación" en algún sentido.

Las variables correlacionadas son aquellas que contienen información sobre las demás. Cuanto más fuerte es la correlación, más nos dice una variable sobre la otra.

Es posible que ya tenga algún conocimiento de la correlación, cómo funciona y cuáles son sus limitaciones. De hecho, es una especie de cliché de la ciencia de datos:

"La correlación no implica causa"

Por supuesto, esto es cierto: hay buenas razones por las que incluso una fuerte correlación entre dos variables no es garantía de causalidad. La correlación observada podría deberse a los efectos de una tercera variable oculta, o simplemente al azar.

Dicho esto, la correlación permite realizar predicciones sobre una variable basándose en otra. Hay varios métodos que se pueden usar para estimar la correlación de datos lineales y no lineales. Echemos un vistazo a cómo funcionan.

Repasaremos las matemáticas y la implementación del código, usando Python y R. El código para los ejemplos de este artículo se puede encontrar aquí.

Coeficiente de correlación de Pearson

¿Qué es?

El coeficiente de correlación de Pearson (PCC, o r de Pearson ) es una medida de correlación lineal ampliamente utilizada. A menudo es el primero que se enseña en muchos cursos de estadísticas elementales. Matemáticamente hablando, se define como “la covarianza entre dos vectores, normalizada por el producto de sus desviaciones estándar”.

Dime más…

La covarianza entre dos vectores emparejados es una medida de su tendencia a variar por encima o por debajo de sus medias. Es decir, una medida de si cada par tiende a estar en lados similares u opuestos de sus respectivos medios.

Veamos esto implementado en Python:

def mean(x): return sum(x)/len(x) def covariance(x,y): calc = [] for i in range(len(x)): xi = x[i] - mean(x) yi = y[i] - mean(y) calc.append(xi * yi) return sum(calc)/(len(x) - 1) a = [1,2,3,4,5] ; b = [5,4,3,2,1] print(covariance(a,b))

La covarianza se calcula tomando cada par de variables y restando sus respectivas medias de ellas. Luego, multiplique estos dos valores juntos.

  • If they are both above their mean (or both below), then this will produce a positive number, because a positive×positive=positive, and likewise a negative×negative=positive.
  • If they are on different sides of their means, then this produces a negative number (because positive×negative=negative).

Once we have all these values calculated for each pair, sum them up, and divide by n-1, where n is the sample size. This is the sample covariance.

If the pairs have a tendency to both be on the same side of their respective means, the covariance will be a positive number. If they have a tendency to be on opposite sides of their means, the covariance will be a negative number. The stronger this tendency, the larger the absolute value of the covariance.

If there is no overall pattern, then the covariance will be close to zero. This is because the positive and negative values will cancel each other out.

At first, it might appear that the covariance is a sufficient measure of ‘relatedness’ between two variables. However, take a look at the graph below:

Looks like there’s a strong relationship between the variables, right? So why is the covariance so low, at approximately 0.00003?

The key here is to realise that the covariance is scale-dependent. Look at the x and y axes — pretty much all the data points fall between the range of 0.015 and 0.04. The covariance is likewise going to be close to zero, since it is calculated by subtracting the means from each individual observation.

To obtain a more meaningful figure, it is important to normalize the covariance. This is done by dividing it by the product of the standard deviations of each of the vectors.

In Python:

import math def stDev(x): variance = 0 for i in x: variance += (i - mean(x) ** 2) / len(x) return math.sqrt(variance) def Pearsons(x,y): cov = covariance(x,y) return cov / (stDev(x) * stDev(y))

The reason this is done is because the standard deviation of a vector is the square root of its variance. This means if two vectors are identical, then multiplying their standard deviations will equal their variance.

Funnily enough, the covariance of two identical vectors is also equal to their variance.

Therefore, the maximum value the covariance between two vectors can take is equal to the product of their standard deviations, which occurs when the vectors are perfectly correlated. It is this which bounds the correlation coefficient between -1 and +1.

Which way do the arrows point?

As an aside, a much cooler way of defining the PCC of two vectors comes from linear algebra.

First, we center the vectors, by subtracting their means from their individual values.

a = [1,2,3,4,5] ; b = [5,4,3,2,1] a_centered = [i - mean(a) for i in a] b_centered = [j - mean(b) for j in b]

Now, we can make use of the fact that vectors can be considered as ‘arrows’ pointing in a given direction.

For instance, in 2-D, the vector [1,3] could be represented as an arrow pointing 1 unit along the x-axis, and 3 units along the y-axis. Likewise, the vector [2,1] could be represented as an arrow pointing 2 units along the x-axis, and 1 unit along the y-axis.

Similarly, we can represent our data vectors as arrows in an n-dimensional space (although don’t try visualising when n > 3…)

The angle ϴ between these arrows can be worked out using the dot product of the two vectors. This is defined as:

Or, in Python:

def dotProduct(x,y): calc = 0 for i in range(len(x)): calc += x[i] * y[i] return calc

The dot product can also be defined as:

Where ||x|| is the magnitude (or ‘length’) of the vector x (think Pythagoras’ theorem), and ϴ is the angle between the arrow vectors.

As a Python function:

def magnitude(x): x_sq = [i ** 2 for i in x] return math.sqrt(sum(x_sq))

This lets us find cos(ϴ), by dividing the dot product by the product of the magnitudes of the two vectors.

def cosTheta(x,y): mag_x = magnitude(x) mag_y = magnitude(y) return dotProduct(x,y) / (mag_x * mag_y)

Now, if you know a little trigonometry, you may recall that the cosine function produces a graph that oscillates between +1 and -1.

The value of cos(ϴ) will vary depending on the angle between the two arrow vectors.

  • When the angle is zero (i.e., the vectors point in the exact same direction), cos(ϴ) will equal 1.
  • When the angle is -180°, (the vectors point in exact opposite directions), then cos(ϴ) will equal -1.
  • When the angle is 90° (the vectors point in completely unrelated directions), then cos(ϴ) will equal zero.

This might look familiar — a measure between +1 and -1 that seems to describe the relatedness of two vectors? Isn’t that Pearson’s r?

Well — that is exactly what it is! By considering the data as arrow vectors in a high-dimensional space, we can use the angle ϴ between them as a measure of similarity.

The cosine of this angle ϴis mathematically identical to Pearson’s Correlation Coefficient.

When viewed as high-dimensional arrows, positively correlated vectors will point in a similar direction.

Negatively correlated vectors will point towards opposite directions.

And uncorrelated vectors will point at right-angles to one another.

Personally, I think this is a really intuitive way to make sense of correlation.

Statistical significance?

As is always the case with frequentist statistics, it is important to ask how significant a test statistic calculated from a given sample actually is. Pearson’s r is no exception.

Unfortunately, whacking confidence intervals on an estimate of PCC is not entirely straightforward.

This is because Pearson’s r is bound between -1 and +1, and therefore isn’t normally distributed. An estimated PCC of, say, +0.95 has only so much room for error above it, but plenty of room below.

Luckily, there is a solution — using a trick called Fisher’s Z-transform:

  1. Calculate an estimate of Pearson’s r as usual.
  2. Transform rz using Fisher’s Z-transform. This can be done by using the formula z = arctanh(r), where arctanh is the inverse hyperbolic tangent function.
  3. Now calculate the standard deviation of z. Luckily, this is straightforward to calculate, and is given by SDz = 1/sqrt(n-3), where n is the sample size.
  4. Choose your significance threshold, alpha, and check how many standard deviations from the mean this corresponds to. If we take alpha = 0.95, use 1.96.
  5. Find the upper estimate by calculating z +(1.96 × SDz), and the lower bound by calculating z - (1.96 × SDz).
  6. Convert these back to r, using r = tanh(z), where tanh is the hyperbolic tangent function.
  7. If the upper and lower bounds are both the same side of zero, you have statistical significance!

Here’s a Python implementation:

r = Pearsons(x,y) z = math.atanh(r) SD_z = 1 / math.sqrt(len(x) - 3) z_upper = z + 1.96 * SD_z z_lower = z - 1.96 * SD_z r_upper = math.tanh(z_upper) r_lower = math.tanh(z_lower)

Of course, when given a large data set of many potentially correlated variables, it may be tempting to check every pairwise correlation. This is often referred to as ‘data dredging’ — scouring the data set for any apparent relationships between the variables.

If you do take this multiple comparison approach, you should use stricter significance thresholds to reduce your risk of discovering false positives (that is, finding unrelated variables which appear correlated purely by chance).

One method for doing this is to use the Bonferroni correction.

The small print

So far, so good. We’ve seen how Pearson’s r can be used to calculate the correlation coefficient between two variables, and how to assess the statistical significance of the result. Given an unseen set of data, it is possible to start mining for significant relationships between the variables.

However, there is a major catch — Pearson’s r only works for linear data.

Look at the graphs below. They clearly show what looks like a non-random relationship, but Pearson’s r is very close to zero.

The reason why is because the variables in these graphs have a non-linear relationship.

We can generally picture a relationship between two variables as a ‘cloud’ of points scattered either side of a line. The wider the scatter, the ‘noisier’ the data, and the weaker the relationship.

However,  Pearson’s r compares each individual data point with only one other (the overall means). This means it can only consider straight lines. It’s not great at detecting any non-linear relationships.

In the graphs above, Pearson’s r doesn’t reveal there being much correlation to talk of.

Yet the relationship between these variables is still clearly non-random, and that makes them potentially useful predictors of each other. How can machines identify this? Luckily, there are different correlation measures available to us.

Let’s take a look at a couple of them.

Distance Correlation

What is it?

Distance correlation bears some resemblance to Pearson’s r, but is actually calculated using a rather different notion of covariance. The method works by replacing our everyday concepts of covariance and standard deviation (as defined above) with “distance” analogues.

Much like Pearson’s r, “distance correlation” is defined as the “distance covariance” normalized by the “distance standard deviation”.

Instead of assessing how two variables tend to co-vary in their distance from their respective means, distance correlation assesses how they tend to co-vary in terms of their distances from all other points.

This opens up the potential to better capture non-linear dependencies between variables.

The finer details…

Robert Brown was a Scottish botanist born in 1773. While studying plant pollen under his microscope, Brown noticed tiny organic particles jittering about at random on the surface of the water he was using.

Little could he have suspected a chance observation of his would lead to his name being immortalized as the (re-)discoverer of Brownian motion.

Even less could he have known that it would take nearly a century before Albert Einstein would provide an explanation for the phenomenon — and hence proving the existence of atoms — in the same year he published papers on E=MC², special relativity and helped kick-start the field of quantum theory.

Brownian motion is a physical process whereby particles move about at random due to collisions with surrounding molecules.

The math behind this process can be generalized into a concept known as the Weiner process. Among other things, the Weiner process plays an important part in mathematical finance’s most famous model, Black-Scholes.

Interestingly, Brownian motion and the Weiner process turn out to be relevant to a non-linear correlation measure developed in the mid-2000’s through the work of Gabor Szekely.

Let’s run through how this can be calculated for two vectors x and y, each of length N.

  1. First, we form N×N distance matrices for each of the vectors. A distance matrix is exactly like a road distance chart in an atlas — the intersection of each row and column shows the distance between the corresponding cities. Here, the intersection between row i and column j gives the distance between the i-th and j-th elements of the vector.

2. Next, the matrices are “double-centered”. This means for each element, we subtract the mean of its row and the mean of its column. Then, we add the grand mean of the entire matrix.

3. With the two double-centered matrices, we can calculate the square of the distance covariance by taking the average of each element in X multiplied by its corresponding element in Y.

4. Now, we can use a similar approach to find the “distance variance”. Remember — the covariance of two identical vectors is equivalent to their variance. Therefore, the squared distance variance can be described as below:

5. Finally, we have all the pieces to calculate the distance correlation. Remember that the (distance) standard deviation is equal to the square-root of the (distance) variance.

If you prefer to work through code instead of math notation (after all, there is a reason people tend to write software in one and not the other…), then check out the R implementation below:

set.seed(1234) doubleCenter <- function(x){ centered <- x for(i in 1:dim(x)[1]){ for(j in 1:dim(x)[2]){ centered[i,j] <- x[i,j] - mean(x[i,]) - mean(x[,j]) + mean(x) } } return(centered) } distanceCovariance <- function(x,y){ N <- length(x) distX <- as.matrix(dist(x)) distY <- as.matrix(dist(y)) centeredX <- doubleCenter(distX) centeredY <- doubleCenter(distY) calc <- sum(centeredX * centeredY) return(sqrt(calc/(N^2))) } distanceVariance <- function(x){ return(distanceCovariance(x,x)) } distanceCorrelation <- function(x,y){ cov <- distanceCovariance(x,y) sd <- sqrt(distanceVariance(x)*distanceVariance(y)) return(cov/sd) } # Compare with Pearson's r x <- -10:10 y  0.057 distanceCorrelation(x,y) # --> 0.509

The distance correlation between any two variables is bound between zero and one. Zero implies the variables are independent, whereas a score closer to one indicates a dependent relationship.

If you’d rather not write your own distance correlation methods from scratch, you can install R’s energy package, written by very researchers who devised the method. The methods available in this package call functions written in C, giving a great speed advantage.

Physical interpretation

One of the more surprising results relating to the formulation of distance correlation is that it bears an exact equivalence to Brownian correlation.

Brownian correlation refers to the independence (or dependence) of two Brownian processes. Brownian processes that are dependent will show a tendency to ‘follow’ each other.

A simple metaphor to help grasp the concept of distance correlation is to picture a fleet of paper boats floating on the surface of a lake.

If there is no prevailing wind direction, then each boat will drift about at random — in a way that’s (kind of) analogous to Brownian motion.

If there is a prevailing wind, then the direction the boats drift in will be dependent upon the strength of the wind. The stronger the wind, the stronger the dependence.

In a comparable way, uncorrelated variables can be thought of as boats drifting without a prevailing wind. Correlated variables can be thought of as boats drifting under the influence of a prevailing wind. In this metaphor, the wind represents the strength of the relationship between the two variables.

If we allow the prevailing wind direction to vary at different points on the lake, then we can bring a notion of non-linearity into the analogy. Distance correlation uses the distances between the ‘boats’ to infer the strength of the prevailing wind.

Confidence Intervals?

Confidence intervals can be established for a distance correlation estimate using a ‘resampling’ technique. A simple example is bootstrap resampling.

This is a neat statistical trick that requires us to ‘reconstruct’ the data by randomly sampling (with replacement) from the original data set. This is repeated many times (e.g., 1000), and each time the statistic of interest is calculated.

This will produce a range of different estimates for the statistic we’re interested in. We can use these to estimate the upper and lower bounds for a given level of confidence.

Check out the R code below for a simple bootstrap function:

set.seed(1234) bootstrap <- function(x,y,reps,alpha){ estimates <- c() original <- data.frame(x,y) N <- dim(original)[1] for(i in 1:reps){ S <- original[sample(1:N, N, replace = TRUE),] estimates <- append(estimates, distanceCorrelation(S$x, S$y)) } u <- alpha/2 ; l <- 1-u interval <- quantile(estimates, c(l, u)) return(2*(dcor(x,y)) - as.numeric(interval[1:2])) } # Use with 1000 reps and threshold alpha = 0.05 x <- -10:10 y  0.237 to 0.546

If you want to establish statistical significance, there is another resampling trick available, called a ‘permutation test’.

This is slightly different to the bootstrap method defined above. Here, we keep one vector constant and ‘shuffle’ the other by resampling. This approximates the null hypothesis — that there is no dependency between the variables.

The ‘shuffled’ variable is then used to calculate the distance correlation between it and the constant variable. This is done many times, and the distribution of outcomes is compared against the actual distance correlation (obtained from the unshuffled data).

The proportion of ‘shuffled’ outcomes greater than or equal to the ‘real’ outcome is then taken as a p-value, which can be compared to a given significance threshold (e.g., 0.05).

Check out the code to see how this works:

permutationTest <- function(x,y,reps){ estimates <- c() observed <- distanceCorrelation(x,y) N <- length(x) for(i in 1:reps){ y_i <- sample(y, length(y), replace = T) estimates <- append(estimates, distanceCorrelation(x, y_i)) } p_value = observed) return(p_value) } # Use with 1000 reps x <- -10:10 y  0.036

Maximal Information Coefficient

What is it?

The Maximal Information Coefficient (MIC) is a recent method for detecting non-linear dependencies between variables, devised in 2011. The algorithm used to calculate MIC applies concepts from information theory and probability to continuous data.

Diving in…

Information theory is a fascinating field within mathematics that was pioneered by Claude Shannon in the mid-twentieth century.

A key concept is entropy — a measure of the uncertainty in a given probability distribution. A probability distribution describes the probabilities of a given set of outcomes associated with a particular event.

To understand how this works, compare the two probability distributions below:

On the left is that of a fair six-sided dice, and on the right is the distribution of a not-so-fair six-sided dice.

Intuitively, which would you expect to have the higher entropy? For which dice is the outcome the least certain? Let’s calculate the entropy and see what the answer turns out to be.

entropy <- function(x){ pr <- prop.table(table(x)) H <- sum(pr * log(pr,2)) return(-H) } dice1 <- 1:6 dice2  2.585 entropy(dice2) # --> 2.281

As you may have expected, the fairer dice has the higher entropy.

That is because each outcome is as likely as any other, so we cannot know in advance which to favour.

The unfair dice gives us more information — some outcomes are much more likely than others — so there is less uncertainty about the outcome.

By that reasoning, we can see that entropy will be highest when each outcome is equally likely. This type of probability distribution is called a ‘uniform’ distribution.

Cross-entropy is an extension to the concept of entropy, that takes into account a second probability distribution.

crossEntropy <- function(x,y){ prX <- prop.table(table(x)) prY <- prop.table(table(y)) H <- sum(prX * log(prY,2)) return(-H) }

This has the property that the cross-entropy between two identical probability distributions is equal to their individual entropy. When considering two non-identical probability distributions, there will be a difference between their cross-entropy and their individual entropies.

This difference, or ‘divergence’, can be quantified by calculating their Kullback-Leibler divergence, or KL-divergence.

The KL-divergence of two probability distributions X and Y is:

The minimum value of the KL-divergence between two distributions is zero. This only happens when the distributions are identical.

KL_divergence <- function(x,y){ kl <- crossEntropy(x,y) - entropy(x) return(kl) }

One use for KL-divergence in the context of discovering correlations is to calculate the Mutual Information (MI) of two variables.

Mutual Information can be defined as “the KL-divergence between the joint and marginal distributions of two random variables”. If these are identical, MI will equal zero. If they are at all different, then MI will be a positive number. The more different the joint and marginal distributions are, the higher the MI.

To understand this better, let’s take a moment to revisit some probability concepts.

The joint distribution of variables X and Y is simply the probability of them co-occurring. For instance, if you flipped two coins X and Y, their joint distribution would reflect the probability of each observed outcome. Say you flip the coins 100 times, and get the result “heads, heads” 40 times. The joint distribution would reflect this.

P(X=H, Y=H) = 40/100 = 0.4

jointDist <- function(x,y){ N <- length(x) u <- unique(append(x,y)) joint <- c() for(i in u){ for(j in u){ f <- x[paste0(x,y) == paste0(i,j)] joint <- append(joint, length(f)/N) } } return(joint) }

The marginal distribution is the probability distribution of one variable in the absence of any information about the other. The product of two marginal distributions gives the probability of two events’ co-occurrence under the assumption of independence.

For the coin flipping example, say both coins produce 50 heads and 50 tails. Their marginal distributions would reflect this.

P(X=H) = 50/100 = 0.5 ; P(Y=H) = 50/100 = 0.5

P(X=H) × P(Y=H) = 0.5 × 0.5 = 0.25

marginalProduct <- function(x,y){ N <- length(x) u <- unique(append(x,y)) marginal <- c() for(i in u){ for(j in u){ fX <- length(x[x == i]) / N fY <- length(y[y == j]) / N marginal <- append(marginal, fX * fY) } } return(marginal) }

Returning to the coin flipping example, the product of the marginal distributions will give the probability of observing each outcome if the two coins are independent, while the joint distribution will give the probability of each outcome, as actually observed.

If the coins genuinely are independent, then the joint distribution should be (approximately) identical to the product of the marginal distributions. If they are in some way dependent, then there will be a divergence.

In the example, P(X=H,Y=H) > P(X=H) × P(Y=H). This suggests the coins both land on heads more often than would be expected by chance.

The bigger the divergence between the joint and marginal product distributions, the more likely it is the events are dependent in some way. The measure of this divergence is defined by the Mutual Information of the two variables.

mutualInfo <- function(x,y){ joint <- jointDist(x,y) marginal <- marginalProduct(x,y) Hjm  0] * log(marginal[marginal > 0],2)) Hj  0] * log(joint[joint > 0],2)) return(Hjm - Hj) }

A major assumption here is that we are working with discrete probability distributions. How can we apply these concepts to continuous data?

Binning

One approach is to quantize the data (make the variables discrete). This is achieved by binning (assigning data points to discrete categories).

The key issue now is deciding how many bins to use. Luckily, the original paper on the Maximal Information Coefficient provides a suggestion: try most of them!

That is to say, try differing numbers of bins and see which produces the greatest result of Mutual Information between the variables. This raises two challenges, though:

  1. How many bins to try? Technically, you could quantize a variable into any number of bins, simply by making the bin size forever smaller.
  2. Mutual Information is sensitive to the number of bins used. How do you fairly compare MI between different numbers of bins?

The first challenge means it is technically impossible to try every possible number of bins. However, the authors of the paper offer a heuristic solution (that is, a solution which is not ‘guaranteed perfect’, but is a pretty good approximation). They also suggest an upper limit on the number of bins to try.

As for fairly comparing MI values between different binning schemes, there’s a simple fix… normalize it! This can be done by dividing each MI score by the maximum it could theoretically take for that particular combination of bins.

The binning combination that produces the highest normalized MI overall is the one to use.

The highest normalized MI is then reported as the Maximal Information Coefficient (or ‘MIC’) for those two variables. Let’s check out some code that will estimate the MIC of two continuous variables.

MIC <- function(x,y){ N <- length(x) maxBins <- ceiling(N ** 0.6) MI  maxBins){ next } Xbins <- i; Ybins <- j binnedX <-cut(x, breaks=Xbins, labels = 1:Xbins) binnedY <-cut(y, breaks=Ybins, labels = 1:Ybins) MI_estimate <- mutualInfo(binnedX,binnedY) MI_normalized <- MI_estimate / log(min(Xbins,Ybins),2) MI <- append(MI, MI_normalized) } } return(max(MI)) } x <- runif(100,-10,10) y  0.751

The above code is a simplification of the method outlined in the original paper. A more faithful implementation of the algorithm is available in the R package minerva. In Python, you can use the minepy module.

MIC is capable of picking out all kinds of linear and non-linear relationships, and has found use in a range of different applications. It is bound between 0 and 1, with higher values indicating greater dependence.

Confidence Intervals?

To establish confidence bounds on an estimate of MIC, you can simply use a bootstrapping technique like the one we looked at earlier.

To generalize the bootstrap function, we can take advantage of R’s functional programming capabilities, by passing the technique we want to use as an argument.

bootstrap <- function(x,y,func,reps,alpha){ estimates <- c() original <- data.frame(x,y) N <- dim(original)[1] for(i in 1:reps){ S <- original[sample(1:N, N, replace = TRUE),] estimates <- append(estimates, func(S$x, S$y)) } l <- alpha/2 ; u <- 1 - l interval  0.594 to 0.88

Summary

To conclude this tour of correlation, let’s test each different method against a range of artificially generated data. The code for these examples can be found here.

Noise

set.seed(123) # Noise x0 <- rnorm(100,0,1) y0 <- rnorm(100,0,1) plot(y0~x0, pch = 18) cor(x0,y0) distanceCorrelation(x0,y0) MIC(x0,y0)
  • Pearson’s r = - 0.05
  • Distance Correlation = 0.157
  • MIC = 0.097

Simple linear

# Simple linear relationship x1 <- -20:20 y1 <- x1 + rnorm(41,0,4) plot(y1~x1, pch =18) cor(x1,y1) distanceCorrelation(x1,y1) MIC(x1,y1)
  • Pearson’s r =+0.95
  • Distance Correlation = 0.95
  • MIC = 0.89

Simple quadratic

# y ~ x**2 x2 <- -20:20 y2 <- x2**2 + rnorm(41,0,40) plot(y2~x2, pch = 18) cor(x2,y2) distanceCorrelation(x2,y2) MIC(x2,y2)
  • Pearson’s r =+0.003
  • Distance Correlation = 0.474
  • MIC = 0.594

Trigonometric

# Cosine x3 <- -20:20 y3 <- cos(x3/4) + rnorm(41,0,0.2) plot(y3~x3, type="p", pch=18) cor(x3,y3) distanceCorrelation(x3,y3) MIC(x3,y3)
  • Pearson’s r = - 0.035
  • Distance Correlation = 0.382
  • MIC = 0.484

Circle

# Circle n <- 50 theta <- runif (n, 0, 2*pi) x4 <- append(cos(theta), cos(theta)) y4 <- append(sin(theta), -sin(theta)) plot(x4,y4, pch=18) cor(x4,y4) distanceCorrelation(x4,y4) MIC(x4,y4)
  • Pearson’s r< 0.001
  • Distance Correlation = 0.234
  • MIC = 0.218

Thanks for reading!