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 el
As you've sent the same question to both lists, please report your results to
both lists.
-Original Message-
From: R-sig-ecology [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of
Karla Shikev
Sent: Friday, May 08, 2015 10:38 AM
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco
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 estima
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 representation
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*
(ashark1*b2*epred2*
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
Shouldn't the analysis account for the different lengths of time? Why
standardize and (I assume) effectively ignore the differences in lengths of
time? Is there an "offset" feature in the proposed analysis as in lm and glmer
that could be used?
Jim
-Original Message-
From: r-sig-eco
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 purp
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
s" fix and/or write the
developer of maxlike and let him or her know about this issue.
Jim
-Original Message-
From: Alok K. Bohara [mailto:boh...@unm.edu]
Sent: Friday, December 07, 2012 7:03 AM
To: Baldwin, Jim -FS; r-sig-ecology@r-project.org
Subject: RE: error in layernames
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, 2
The problem seems to be the way you are generating the 0's and 1's and not
colext in unmarked.
One can obtain a rough estimate the colonization and extinction probabilities
from any two neighboring pairs of seasons (although in practice you'd want to
use all of the data). Take seasons 29 and 3
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 (lat,
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
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
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