[Vegan-commits] r958 - pkg/vegan/man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Thu Aug 27 09:58:24 CEST 2009


Author: jarioksa
Date: 2009-08-27 09:58:23 +0200 (Thu, 27 Aug 2009)
New Revision: 958

Modified:
   pkg/vegan/man/adonis.Rd
Log:
anosim.Rd formatting: now identical to branches/1.15

Modified: pkg/vegan/man/adonis.Rd
===================================================================
--- pkg/vegan/man/adonis.Rd	2009-08-26 15:58:07 UTC (rev 957)
+++ pkg/vegan/man/adonis.Rd	2009-08-27 07:58:23 UTC (rev 958)
@@ -60,10 +60,15 @@
 \code{\link[ade4]{amova}} in the \pkg{ade4} package) for both crossed
 and nested factors.
 
+If the experimental design has nestedness, then use \code{strata} to
+test hypotheses. For instance, imagine we are testing the whether a
+plant community is influenced by nitrate amendments, and we have two
+replicate plots at each of two levels of nitrate (0, 10 ppm). We have
+replicated the experiment in three fields with (perhaps) different
+average productivity. In this design, we would need to specify
+\code{strata = field} so that randomizations occur only \emph{within
+each field} and not across all fields . See example below.
 
-If the experimental design has nestedness, then use \code{strata} to test hypotheses. For instance, imagine we are testing the whether a plant community is influenced by nitrate amendments, and we have two replicate plots at each of two levels of nitrate (0, 10 ppm). We have replicated the experiment in three fields with (perhaps) different average productivity. In this design, we would need to specify \code{strata = field} so that randomizations occur only \emph{within each field} and not across all fields . See example below.
-
-
 Like AMOVA (Excoffier et al. 1992), \code{adonis} relies on a
 long-understood phenomenon that allows one to partition sums of squared
 deviations from a centroid in two different ways (McArdle and Anderson
@@ -193,8 +198,6 @@
 
 ### Incorrect (no strata)
 adonis(Y ~ NO3, data=dat, perm=1e3)
-
-
 }
 
 \keyword{multivariate }



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