[R] Scale time series in a way that 90% of the data is in the -0.-9/ +0.9 range

[Mr.Q]

Hello,

How can i scale my time series in a way that 90% of the data is in the
-0.-9/ +0.9 range?

My approach is to first build a clean vector without those 10% far
away from the mean

require(outliers)

y<-rep(c(1,1,1,1,1,9),10)
yc<-y

ycc<-length(y)*0.1

for(j in 1:ycc)
{
	cat("Remove",j)
	yc<-rm.outlier(yc)
}

and then do my scaling based on the cleaned data

for(k in 1:length(y)) {
	y[k]<-(((y[k]-min(yc))/(max(yc)-min(yc)))*1.8)-0.9
}

This works fine for the first three loops, but then strangely crashes :
(

Remove 1Remove 2Remove 3Error in if (xor(((max(x, na.rm = TRUE) -
mean(x, na.rm = TRUE)) < (mean(x,  :
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In max(x, na.rm = TRUE) :
  no non-missing arguments to max; returning -Inf
2: In min(x, na.rm = TRUE) :
  no non-missing arguments to min; returning Inf

Any ideas for me?

Thanks in advance,

Mr. Q