One metric for an "average width" that would be quick to calculate might be the
diameter of a circle that has the same area as the polygon. (Of course, if the
tree crowns are nowhere near circular, this won't likely be a useful metric.)
Maybe there might be a similar approach for finding an
Yes, the analysis with a small sample size would be valid (under the assumption
that the model - both fixed and random effects are correctly specified) but at
some point there must be a practical assessment as to the desired precision and
the costs of the consequences of either inadequate
The sentence alternative hypothesis: true difference in means is not equal to
0 is stating what the alternative hypothesis is and not that your particular
difference in means is significantly different from zero. That sentence would
appear (when you have a two-tailed test) no matter what the
An explicit formula for a posterior distribution is not something to expect
from an MCMC procedure. But the next best thing to an explicit formula for a
posterior distribution is a zillion samples from that distribution (which is
what you have).
What you can do is display smooth
Using Mathematica I get the following:
H1 - (apred1*ashark2*eshark2*(ashark3*b2+ashark2*xpred)+
ashark3*eshark3*(-(alpha22*b2*(ashark3*b1+ashark1*xpred))+
alpha12*b1*(ashark3*b2+ashark2*xpred))-alpha22*apred1*ashark3*b2*xshark+
apred2^2*ashark1*epred2*xshark-apred2*(-(ashark3*
Do you have multiple measurements on individuals? If so, have you accounted
for the repeated measures? If there are no multiple measurements on
individuals, then maybe what explains a decrease in body size is a selection
bias: only smaller individuals survive in later time periods resulting
I assume that the dataset island is yours so I've made that equivalent to an
example dataset in vegan named dune. Below is a rather brute force way to
assign colors.
library(MASS)
library(vegan)
data(dune)
island = dune
island.NMDS - metaMDS(island,k=2, distfun = betadiver,
distance =
Two additional issues might be considered:
1. Correlated variables are still correlated after PCA or after tossing one of
the variables so teasing apart separate effects of the two variables is not
resolved (nor can it necessarily be resolved with the particular dataset at
hand).
2. The
I suspect it must be a version problem. Running Windows XP (yes, still XP)
with R version 2.13.1 (2011-07-08) and just now loading vegan and BiodiversityR
I get the following:
library(BiodiversityR)
Loading required package: vegan
Loading required package: permute
This is vegan 2.0-2
Warning
I think you might want
names(r) - c(x1, x2, x3)
rather than
r@layernames - c(x1, x2, x3)
Jim
-Original Message-
From: r-sig-ecology-boun...@r-project.org
[mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Alok K. Bohara
Sent: Thursday, December 06, 2012 10:26 AM
Because that road has a few bends, you'll need to get more points (just lat and
long not necessarily KM) on that road just as you've done at the plaques. That
would give you a set of connected nodes (as suggested below by David Valentim
Dias) that would better approximate the road.
Those
Here is an alternative approach that takes a few more steps and assumes the
original data frame is named d0:
# Split into two data frames
d1 - d0[,c(1:3)]
d2 - d0[,c(1,4,5)]
# Rename the columns in the second one so that the names match
names(d2) - names(d1)
# Concatenate the two identically
Sorry. It looked fine when I sent it. Below I've added back in the line feeds
that somehow disappeared.
Jim
-Original Message-
From: r-sig-ecology-boun...@r-project.org
[mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Baldwin, Jim -FS
Sent: Friday, June 29, 2012 10:28 AM
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