MGC Statistic

library(mgc)
library(reshape2)
library(ggplot2)

plot_sim_func <- function(X, Y, Xf, Yf, name, geom='line') {
  if (!is.null(dim(Y))) {
    Y <- Y[, 1]
    Yf <- Yf[, 1]
  }
  if (geom == 'points') {
    funcgeom <- geom_point
  } else {
    funcgeom <- geom_line
  }
  data <- data.frame(x1=X[,1], y=Y)
  data_func <- data.frame(x1=Xf[,1], y=Yf)
  ggplot(data, aes(x=x1, y=y)) +
    funcgeom(data=data_func, aes(x=x1, y=y), color='red', size=3) +
    geom_point() +
    xlab("x") +
    ylab("y") +
    ggtitle(name) +
    theme_bw()
}

plot_mtx <- function(Dx, main.title="Local Correlation Map", xlab.title="# X Neighbors", ylab.title="# Y Neighbors") {
  data <- melt(Dx)
  ggplot(data, aes(x=Var1, y=Var2, fill=value)) +
    geom_tile() +
    scale_fill_gradientn(name="l-corr",
                         colours=c("#f2f0f7", "#cbc9e2", "#9e9ac8", "#6a51a3"),
                         limits=c(min(Dx), max(Dx))) +
    xlab(xlab.title) +
    ylab(ylab.title) +
    theme_bw() +
    ggtitle(main.title)
}

MGC Statistic

In this notebook, we show how to use the MGC statistic in a real and simulated context.

Simulated Data

First, we use a simulated example, where Y = XB + N; that is, Y is linearly dependent on X with added gaussian noise.

n=200 # 100 samples
d=1 # simple 1-d case

set.seed(12345)
data <- mgc.sims.linear(n, d)  # data with noise
func <- mgc.sims.linear(n, d, eps=0)  # source function

We first visualize the data:

plot_sim_func(data$X, data$Y, func$X, func$Y, name="Linear Simulation")

which clearly shows a slightly linear relationship.

visualizing the MGC image with 100 replicates:

set.seed(12345)
res <- mgc.test(data$X, data$Y, nperm=20)  # 20 permutations test; typically should be run with >100 permutations
## Warning in mgc.test(data$X, data$Y, nperm = 20): nperm is < 100. nperm should
## typically be set > 100.
plot_mtx(res$localCorr, main.title="Local Correlation Map")

print(res$optimalScale)
## $x
## [1] 200
## 
## $y
## [1] 200
print(res$stat)
## [1] 0.2870046

As we can see, the local correlation plot suggests a strongly linear relationship. This is because intuitively, having more and more neighbors will help in our identification of the linear relationship between x and y, and as we can see in the local correlation map, k=n=200 and l=n=200 shows the best smoothed p.

Real Data

In the below demo, we show the result of MGC to determine the relationship between the first (sepal length) and third (petal length) dimensions of the iris dataset:

set.seed(12345)
res <- mgc.test(iris[,1,drop=FALSE], iris[,3,drop=FALSE], nperm=20)
## Warning in mgc.test(iris[, 1, drop = FALSE], iris[, 3, drop = FALSE], nperm =
## 20): nperm is < 100. nperm should typically be set > 100.
plot_mtx(res$localCorr, main.title="Local Correlation Map",
         xlab.title="Sepal Length Neighbors", ylab.title="Petal Length Neighbors")

print(res$optimalScale)
## $x
## [1] 35
## 
## $y
## [1] 43
print(res$statMGC)
## NULL

viewing the corr map above we see that the relationship betweel Sepal and Petal Length is strongly linear.