This function computes a weighted sum for a matrix based on computed quantiles and user defined relative importance.
rg.wtdsums(x, ri, xcentr = NULL, xdisp = NULL)
matrix
vector for the relative importance, length(ri)=length(x[1,])
the provided center
the provided variance
input parameter
the center
the variance
relative importance
weights
normalized weights
normalized weights times standardized x
It is not necessary to provide the center and the variance. If those values are not supplied the center is the 50% quantile and the variance is calculated from the 25% and 75% quantile.
C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.
# NOT RUN {
data(chorizon)
var=c("Si_XRF","Al_XRF","K_XRF","LOI","P","Mn")
ri=c(-2.0,1.5,2.0,2.0,3.0,2.0)
x=chorizon[,var]
rg.wtdsums(x,ri)
# }
Run the code above in your browser using DataLab