Date: Mon, 1 Nov 2010 17:38:54 -0700
From: djmu...@gmail.com
To: cy...@email.arizona.edu
CC: r-help@r-project.org
Subject: Re: [R] question in using nlme and lme4 for unbalanced data
Hi:
On Mon, Nov 1, 2010 at 3:59 PM, Chi Yuan wrote:
Hello:
I need some help about using mixed for model for unbalanced data. I
have an two factorial random block design. It's a ecology
experiment. My two factors are, guild removal and enfa removal. Both
are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But
within each block, it's
Hi:
On Mon, Nov 1, 2010 at 3:59 PM, Chi Yuan cy...@email.arizona.edu wrote:
Hello:
I need some help about using mixed for model for unbalanced data. I
have an two factorial random block design. It's a ecology
experiment. My two factors are, guild removal and enfa removal. Both
are two
Chi Yuan cyuan at email.arizona.edu writes
Hello:
I need some help about using mixed for model for unbalanced data. I
have an two factorial random block design. It's a ecology
experiment. My two factors are, guild removal and enfa removal. Both
are two levels, 0 (no removal), 1 (removal).
Hello:
I have an two factorial random block design. It's a ecology
experiment. My two factors are, guild removal and enfa removal. Both
are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But
within each block, it's unbalanced at plot level because I have 5
plots instead of 4 in each
Chi:
I would say that these are not really R questions, but about
statistical procedures in general. However, there are some important
issues that you touch on, so I cannot resist commenting. Hopefully my
comments might pique others to add theirs -- and perhaps to disagree
with me.
1. Unbalanced
Bert Gunter gunter.berton at gene.com writes:
Thanks for starting this. I don't really disagree, much.
I would say that these are not really R questions, but about
statistical procedures in general. However, there are some important
issues that you touch on, so I cannot resist commenting.
On 10-10-25 04:59 PM, Bert Gunter wrote:
...ignore the block variation entirely,
If the between block variability is large, this will lose precision;
with imbalance, it could also result in bias (prhaps not in this
study...). The mixed or fixed effects choice is arbitrary; this is not
--
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