wtd.rowMeans: Weighted Mean of each Row - WORK IN PROGRESS

In ejanalysis/analyze.stuff: Miscellaneous Tools for Analyzing Data in Rows and Columns

Weighted Mean of each Row - WORK IN PROGRESS

Description

Returns weighted mean of each row of a data.frame or matrix, based on specified weights, one weight per column.

Usage

wtd.rowMeans(x, wts = 1, na.rm = FALSE, dims = 1)

Arguments

x	Data.frame or matrix, required.
wts	Weights, optional, defaults to 1 which is unweighted, numeric vector of length equal to number of columns
na.rm	Logical value, optional, TRUE by default. Defines whether NA values should be removed before result is found. Otherwise result will be NA when any NA is in a vector.
dims	dims=1 is default. Not used. integer: Which dimensions are regarded as 'rows' or 'columns' to sum over. For row*, the sum or mean is over dimensions dims+1, ...; for col* it is over dimensions 1:dims.

Value

Returns a vector of numbers of length equal to number of rows in df.

See Also

wtd.colMeans() wtd.rowMeans() wtd.rowSums() rowMaxs() rowMins() colMins()

Examples

x=data.frame(a=c(NA, 2:10), b=rep(100,10), c=rep(3,10))
w=c(1.1, 2, NA)
cbind(x, wtd.rowMeans(x, w) )
cbind(x, wtd.rowSums(x, w) )
x=data.frame(a=c(NA, 2:4), b=rep(100,4), c=rep(3,4))
w=c(1.1, 2, NA, 0)
print(cbind(x,w, wtd=w*x))
print(wtd.colMeans(x, w, na.rm=TRUE))
#rbind(cbind(x,w,wtd=w*x), c(wtd.colMeans(x,w,na.rm=TRUE), 'wtd.colMeans', rep(NA,length(w))))

x=data.frame(a=c(NA, 2:10), b=rep(100,10), c=rep(3,10))
w=c(1.1, 2, NA, rep(1, 7))
print(cbind(x,w, wtd=w*x))
rbind(cbind(x, w), cbind(wtd.colMeans(x, w, na.rm=TRUE), w='wtd.colMeans') )
print(w*cbind(x,w))

ejanalysis/analyze.stuff documentation built on April 2, 2024, 10:10 a.m.