Nothing
## ---- message=FALSE-----------------------------------------------------------
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)
}
## -----------------------------------------------------------------------------
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
## ---- fig.width=5, fig.height=3-----------------------------------------------
plot_sim_func(data$X, data$Y, func$X, func$Y, name="Linear Simulation")
## ---- fig.width=5, fig.height=4-----------------------------------------------
set.seed(12345)
res <- mgc.test(data$X, data$Y, nperm=20) # 20 permutations test; typically should be run with >100 permutations
plot_mtx(res$localCorr, main.title="Local Correlation Map")
print(res$optimalScale)
print(res$statMGC)
## ---- fig.width=5, fig.height=4-----------------------------------------------
set.seed(12345)
res <- mgc.test(iris[,1,drop=FALSE], iris[,3,drop=FALSE], nperm=20)
plot_mtx(res$localCorr, main.title="Local Correlation Map",
xlab.title="Sepal Length Neighbors", ylab.title="Petal Length Neighbors")
print(res$optimalScale)
print(res$statMGC)
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