Dear Radka
I'm not sure I quite understand your design and quite where the nesting comes
in.
But a quick suggestion is why are you adding species as random as well as
fixed? I don't think you can do this or indeed should do it. I think this is
why you get problems with your fixed effects. If
Hi,
I wonder if anyone can help me with specifying a right model for my analysis. I
am a beginner to lme methods and though have spent already many hours studying
from various books an on-line helps, I was unfortunately not able to find a
solution to my problem on my own.
Data structure:
I
Dear R-help,
I am sorry if this is more of a stats question than an R-question, but I have
found it difficult to get a clear answer by other means.
Q. Would it be wrong to specify a nested model and retain a common intercept,
e.g.
lm(NH4 ~ Site/TideCode + 1)
I am aware (?) that my
Jorn,
For your model,
model-lme(Biomass~Age,random=~1|Age/Stand)
think about nesting age in stand (doesn't that makes more sense, anyway ?). If
you're lucky the NaN will zip. So, do
model - lme( Biomass ~ Age, random = ~ 1 | Stand/Age
I've had a similar problem with unbalanced data when
Hello,
there is a problem to calculate the following model:
model-aov(Biomass~Beech+Age+Error(Age/Stand))
Warning message:
Error() model is singular in: aov(Biomass ~ Beech + Age + Error(Age/Stand))
The summary output is:
Error: Age
Df Sum Sq Mean Sq
Beech 1 142671 142671
Error:
Your model
model - aov(Biomass ~ Beech + Age + Error(Age/Stand))
has a redundancy that might be causing the problem. I can't tell
without the data.
Try
tree.aov - aov(Biomass ~ Beech + Age + Error(Stand %in% Age))
A second potential problem is the class of the variables.
From the degrees of
Dear list members,
First of all thank you for your helpful advices.
After your answeres to my firt mail I studied a lot (R-News n°5) and I
tried to perform my analysis:
First, to fit a GLM with a nested design I decided to use the function
lmer in package lme4
as suggested by Spencer Graves and
see inline
Giovanni Bacaro wrote:
Dear list members,
I'd like to perform a glm analysis with a hierarchically nested design. In
particular,
I have one fixed factor (Land Use Classes) with three levels and a random
factor (quadrat) nested within Land Use Classes with different levels per
Dear list members,
I'd like to perform a glm analysis with a hierarchically nested design. In
particular,
I have one fixed factor (Land Use Classes) with three levels and a random
factor (quadrat) nested within Land Use Classes with different levels per
classes (class artificial = 1 quadrat;
Steeno, Gregory S [EMAIL PROTECTED] writes:
I'm a SAS user who is slowly but surely migrating over to R. I'm trying to
find the proper code to analyze a nested design. I have four
classification variables, L (fixed), A (random within L), D (random within
L), and I (random within L). The
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