mean.acomp {compositions} | R Documentation |
Compute the mean in the several approaches of compositional and amount data analysis.
mean.acomp(x,..., na.action=get(getOption("na.action"))) mean.rcomp(x,..., na.action=get(getOption("na.action"))) mean.aplus(x,..., na.action=get(getOption("na.action"))) mean.rplus(x,..., na.action=get(getOption("na.action"))) mean.rmult(x,..., na.action=get(getOption("na.action")))
x |
a classed dataset of amounts or compositions |
... |
further arguments to mean e.g. trim |
na.action |
The na.action to be used: one of
na.omit ,na.fail ,na.pass |
The different compositional approaches acomp
,
rcomp
,
aplus
, rplus
correpond to different
geometries. The mean is calculated in the respective canonical
geometry by applying a canonical transform (see cdt
), taking ordinary
mean.col
and backtransforming.
The Aitchison geometries imply that mean.acomp
and mean.aplus
are
geometric means, the first one closed. The real geometry implies that
mean.rcomp
and mean.rplus
are arithmetic means, the first
one resulting in a closed composition.
In all cases the mean is again an object of the same class.
The mean is given as a composition or amount vector of the same class as the original dataset.
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
clo
, mean.col
,
geometricmean
, acomp
,
rcomp
, aplus
, rplus
data(SimulatedAmounts) mean.col(sa.lognormals) mean(acomp(sa.lognormals)) mean(rcomp(sa.lognormals)) mean(aplus(sa.lognormals)) mean(rplus(sa.lognormals)) mean(rmult(sa.lognormals))