Kjetil Brinchmann Halvorsen wrote:
Jason W. Martinez wrote:
If you only concentrate on the relative proportions, this are called compositional data. I f your data are inDear R-users,
I have an outcome variable and I'm unsure about how to treat it. Any advice?
mydata (n x 4), you obtain compositions by
sweep(mydata, 1, apply(mydata, 1, sum), "/")
There are not (AFAIK) specific functions/packages for R for compositional data AFAIK, but you
can try googling. Aitchison has a monography (Chapman & Hall) and a paper in JRSS B.
One way to start might be lm's or anova on the symmetric logratio transform of the
compositons. The R function lm can take a multivariate response, but some extra programming will be needed
for interpretation. With simulated data:
> slr function(y) { # y should sum to 1 v <- log(y) return( v - mean(v) ) } > testdata <- matrix( rgamma(120, 2,3), 30, 4) > str(testdata) num [1:30, 1:4] 0.200 0.414 0.311 2.145 0.233 ... > comp <- sweep(testdata, 1, apply(testdata,1,sum), "/") # To get the symmetric logratio transform: comp <- t(apply(comp, 1, slr)) # Observe: apply(cov(comp), 1, sum) [1] -5.551115e-17 2.775558e-17 5.551115e-17 -2.775558e-17 > lm( comp ~ 1)
Call: lm(formula = comp ~ 1)
Coefficients:
[,1] [,2] [,3] [,4] (Intercept) 0.17606 0.06165 -0.03783 -0.19988
Followup:
> mmod <- manova(comp ~ x) > summary(mmod) Error in summary.manova(mmod) : residuals have rank 3 < 4 >
So the manova() function cannot be used. I guess MANOVA for compositional data should be
a straight extension, but it must be programmed , standard manova cannot be used.
Kjetil
--
Kjetil Halvorsen.
Peace is the most effective weapon of mass construction.
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