Dear R- users and Helpers
Is there some way to re initialise or clear the array elements? Pardon me for being
vague but the problem itself quite vague. I have attached the code along with email.
When I run the saved r- code using source("random1.txt") , command.
The program runs fine..but at times there is an error see below # ; but again after
the error if re-excuted it would work fine…I probably missed some array detail in my
program any suggestions
#Error in var(parrX[1, ]) : missing observations in cov/cor
parrX[] is an array .
2) Also pardon me for the lengthy procedural code
but as you could see in it..i have used the for loop for finding the positions
(indexes) of the missing values and later carry out updating the new array element
values at those particular positions/
So how can I escape the for loop in this case ? i.e get the missing position
indexes and save another object ay vector or array ?
And also later wanted to use matrix or vector multiplication (%*%) for the
updating statement
newy[i]<- u2 + covXY/varX * (sample$x[i] - u1)
is any of the apply function good out here ?
I really feel that I am doing something very routine and donkey work and I am most
certain that powerful R – functions could just execute the same 10 liner for loop
condition to mere 4 lines ? but how…I am getting lost in the sea of functions here…
Thank u for reading
Regards
Kunal
# creation for random data set
x <- rnorm(100,17,24)
y <- rnorm(100,7,11)
A <- matrix(c(10,25,8,40),nrow=2,ncol=2)
z <- rbind(x, y) # now 2 x 100
w <- A %*% z # 2 x 100
x <- w[1,]
y <- w[2,]
print("meanX meanY varX varY covar")
# mean of the variates
u1 <- mean(x)
u2 <- mean(y)
# Variances of the variates
varX <- var(x)
varY <- var(y)
# coVariances of the variates
covXY <- cov(x,y)
# printing mean , var , covar
print(c(u1,u2,varX,varY,covXY))
# now replace randomly with NA
x[sample(1:length(x), 10)] <- NA
y[sample(1:length(y), 10)] <- NA
# just a variable
sample <- data.frame("x"=x,"y"=y)
# Vector for missing values
missing <- is.na(sample$x) | is.na(sample$y)
xNA <- array(sample$x[missing], dim=c(1,length(sample$x[missing])))
yNA <- array(sample$y[missing], dim=c(1,length(sample$y[missing])))
# find the mean of the non missing values in the array
mX <- mean(sample$x,na.rm=TRUE)
mY <- mean(sample$y,na.rm=TRUE)
# Imputing the missing values with current estimated means
x<-array(ifelse(is.na(sample$x), mX, sample$x),dim=c(1,length(sample$x)))
y<-array(ifelse(is.na(sample$y), mY, sample$y),dim=c(1,length(sample$y)))
print(" meanX meanY varX varY covar")
# algorithm function to find out new estimate values
algoResult <- function(parrX,parrY,parrXNA,parrYNA,ictr) {
# Variables for New X and Y
newx <- array(parrX, dim=c(1,length(parrX)))
newy <- array(parrY, dim=c(1,length(parrY)))
# mean of the variates
u1 <- mean(parrX)
u2 <- mean(parrY)
# Variances of the variates
varX <- var(parrX[1,])
varY <- var(parrY[1,])
# coVariances of the variates
covXY <- cov(parrX[1,],parrY[1,])
# printing mean , var , covar
print(c(u1,u2,varX,varY,covXY))
# Expected or updated values for the missing values
# loop for finding positon of missing vectors
for (i in 1:length(missing)) {
if (missing[i]==TRUE)
{
if (is.na(sample$x[i]== TRUE))
{
newx[i] <- u1 + covXY/varY * (sample$y[i] -u2)
}
if (is.na(sample$y[i]== TRUE))
{
newy[i]<- u2 + covXY/varX * (sample$x[i] - u1)
}
}
}
l <- list(c(newx),c(newy))
names(l)<-c("X1","Y1"); as.data.frame(l)
return(l)
}
# the iteration loop to do the algorithm
for (ctr in 1:50) {
Output <- algoResult(x,y,xNA,yNA,ctr); Output
x <- array(Output$X1, dim=c(1,length(Output$X1)))
y <- array(Output$Y1, dim=c(1,length(Output$Y1)))
}
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