matrix.na <- function (x, y) {
nvar <- ncol(x)
n.obs = nrow(x)
ny <- ncol(y)
if(nvar!= NROW(y)) stop
result <- matrix(NA,ncol=ny,nrow=n.obs)
for (k in 1:n.obs) {
  for (i in 1:ny ) {
   result[k,i] <- mean(x[k,] * y[,i],na.rm=TRUE)}
   }
result
}

#an attempt to do fast matrixlike operation with missing data\

matrixMult.na <- function(x,y,scale=TRUE) {
#first get tranpose x to make multiplication work

nvar <- ncol(x)
if(nvar != nrow(y) ) stop("matrices are not compatible")#matrices are not compatible

if(scale) x <- scale(x) #zero center and standaridize
tx <- t(x) #we want to do  it on the transposed matrix
ny <- ncol(y)
result <- matrix(NA,nrow = nrow(x),ncol= ncol(y))
result <- apply(y,2,function(x ) colSums(x * tx,na.rm=TRUE))
return((result))

}

"impute.na" <- function(x,impute="mean") {
  miss <- which(is.na(x),arr.ind=TRUE)
   if(impute=="mean") {
       		item.means <- colMeans(x,na.rm=TRUE)   #replace missing values with means
       		x[miss]<- item.means[miss[,2]]} else { 
       		item.med   <- apply(items,2,median,na.rm=TRUE) #replace missing with medians
        	x[miss]<- item.med[miss[,2]]} 
    return(x)
 }