I have a 2396x34 double matrix named y wherein each row (2396) represents a separate situation consisting of 34 consecutive time segments.
I also have a numeric[34] named x that represents a single situation of 34 consecutive time segments.
Currently I am calculating the correlation between each row in y and x like this:
crs[,2] <- cor(t(y),x)
What I need now is to replace the cor function in the above statement with a weighted correlation. The weight vector xy.wt is 34 elements long so that a different weight can be assigned to each of the 34 consecutive time segments.
I found the Weighted Covariance Matrix function cov.wt and thought that if I first scale the data it should work just like the cor function. In fact you can specify for the function to return a correlation matrix as well. Unfortunately it does not seem like I can use it in the same manner because I cannot supply my two variables (x and y) separately.
Does anyone know of a way I can get a weighted correlation in the manner I described without sacrificing much speed?
Edit: Perhaps some mathematical function could be applied to y prior to the cor function in order to get the same results that I'm looking for. Maybe if I multiply each element by xy.wt/sum(xy.wt)?
Edit #2 I found another function corr in the boot package.
corr(d, w = rep(1, nrow(d))/nrow(d))
d
A matrix with two columns corresponding to the two variables whose correlation we wish to calculate.
w
A vector of weights to be applied to each pair of observations. The default is equal weights for each pair. Normalization takes place within the function so sum(w) need not equal 1.
This also is not what I need but it is closer.
Edit #3 Here is some code to generate the type of data I am working with:
x<-cumsum(rnorm(34))
y<- t(sapply(1:2396,function(u) cumsum(rnorm(34))))
xy.wt<-1/(34:1)
crs<-cor(t(y),x) #this works but I want to use xy.wt as weight
You can go back to the definition of the correlation.
f <- function( x, y, w = rep(1,length(x))) {
stopifnot( length(x) == dim(y)[2] )
w <- w / sum(w)
# Center x and y, using the weighted means
x <- x - sum(x*w)
y <- y - apply( t(y) * w, 2, sum )
# Compute the variance
vx <- sum( w * x * x )
vy <- rowSums( w * y * y ) # Incorrect: see Heather's remark, in the other answer
# Compute the covariance
vxy <- colSums( t(y) * x * w )
# Compute the correlation
vxy / sqrt(vx * vy)
}
f(x,y)[1]
cor(x,y[1,]) # Identical
f(x, y, xy.wt)