generally works. However, I am thinking this is a messy
approach and I am not sure I am achieving the idea of using the reference lines
as benchmarks for phenotype scores. Any thoughts and ideas are most welcome.
Thanks in advance
Allan Edelsparre
[[alternative HTML version deleted
Hi all,
I have two data sets I want to merge. One has a vector with trap IDs and the
vector from the other data set has the number of individuals captured at each
trap site. I need to reverse the trap ID's within the trap ID vector in order
to match the number trapped. Here is an example of how
Thanks Peter,
I think that might actually work. I Will let you know.
Allan
- Original Message -
From: "Peter Solymos"
To: "Allan Edelsparre"
Cc: r-sig-ecology@r-project.org
Sent: Saturday, August 18, 2012 10:50:46 AM
Subject: Re: [R-sig-eco] Randomizing matr
Dear R ecologists,
I'm trying to figure out a way to calculate the sum of squared
distances (SSD) between two matrices, where one matrix is held
constant and the other is randomized. So far I have been able to get
the syntax together to obtain my observed SSD, but the problem for me
is obtain SSD
It was suggested that I post the code I used to construct the model
This is how it was constructed:
time <- factor(rep(c("rep1", "rep2"), c(36, 36)),levels=c("rep1", "rep2"))
morph <- ordered(rep(1:36, 2))
idata <- data.frame(time, morph)
model2<-
lm(cbind(a1,b1,c1,d1,e1,f1,g1,h1,i1,j1,k1,l1,m
Hi all,
I am in a situation where I want to ask what is the effect of ecomorph, diet,
and family(nested in ecomorph) on a number of behaviours that are repeated
measures. I use the Car package to run the MANOVA and it worked on my previous
data set. However, on this new data set I get the fol
Hi all,
I am in a situation where I want to ask what is the effect of ecomorph, diet,
and family(nested in ecomorph) on five behaviours that are repeated measures.
Because I have repeated measures I was wondering if the mixed effects model
command (lme) can handle a MANOVA, or can the MANOVA c
I have a problem with quantile regression when I want to analyse quantiles
above the 90th quantile and below the 5 quantile. When I check for significance
I use the following commands:
>results<- rq(Total~Pmove, tau = 0.9)
>summary(results, se="nid")
Call: rq(formula = Total ~ Pmove, tau = 0.9)