At 01:59 PM 18/07/2005, Addison, Prue wrote:
Hi,
I am having trouble using the lme function to perform a nested ANOVA
with a random nested factor.
My design is as follows:
Location (n=6) (Random)
Site nested within each Location (n=12) (2 Sites nested within each
Location) (Random)
Dependent variable: sp (species abundance)
By using the aov function I can generate a nested ANOVA, however this
assumes that my nested factor is fixed.
summary(aov(sp~Location/Site, data=mavric))
Df Sum Sq Mean Sq F value Pr(F)
Location 4 112366 28092 1.2742 0.2962
Location:Transect 5 121690 24338 1.1039 0.3736
Residuals 40 881875 22047
Yes, this is equivalent to aov(sp ~ Location + Location:Site,...)
I have tried the following lme function to specify that Site is random:
lme1 - lme(sp~Location, random=~1|Site, data=mavric)
Here, Location is fixed, and Site is a grouping factor. There are fixed and
random components to the intercept.
lme2 - lme(sp~Location, random=~1|Location/Site, data=mavric)
Here you have Location as a fixed effect, the intercept is random and the
grouping is Site %in % Location.
anova(lme1)
numDF denDF F-value p-value
(Intercept) 140 3.418077 0.0719
Location4 5 1.152505 0.4294
How can you have 6 sites, but only 4 df for Location (should be 5?)
This gives me the correct F-value for Location from
MSLocation/MSLocation:Transect, but the p-value doesn't seem to be
correct (by my calculations in Microsoft Excel it should be 0.345)
I don't know your data, or your calculations. Microsoft Excel does not fill
me with confidence.
anova(lme2)
Warning in pf(q, df1, df2, lower.tail, log.p) :
NaNs produced
Warning: NAs introduced by coercion
numDF denDF F-value p-value
(Intercept) 140 0.459966 0.5015
Location4 0 0.155091 NaN
? I don't know what this output means
Division by zero, somewhere? :-)
anova(lme1,lme2)
Model df AIC BIClogLik Test L.Ratio p-value
lme1 1 7 603.7534 616.4000 -294.8767
lme2 2 8 605.7534 620.2067 -294.8767 1 vs 2 1.815674e-05 0.9966
? I also don't know what this output means.
you are testing the difference between the models, using a likelihood ratio
test. The difference is not significant, so the conventional wisdom is to
choose the simpler model (lme1). Note that the AIC and BIC are lower
(better) for lme1, too.
Can anyone tell me if there is a way to use the lme() function in order
to obtain the same output as the aov() function (above), but so it
correctly calculates the MS, F and p values for my main Location factor?
The following code shows agreement:
dat - data.frame(Location=factor(rep(1:6, each=2)), Site=factor(rep(1:2,
12)), sp=rnorm(12))
fit - aov(sp ~ Location + Error(Site), data=dat)
fit2 - lme(sp ~ Location, random=~1|Site, data=dat)
summary(fit)
anova(fit2)
HTH,
Simon.
Thanks,
Prue
This e-mail is solely for the named addressee and may be confidential.
You should only read, disclose, transmit, copy, distribute, act in reliance
on or commercialise the contents if you are authorised to do so. If you
are not the intended recipient of this e-mail, please notify
[EMAIL PROTECTED] by e-mail immediately, or notify the sender
and then destroy any copy of this message. Views expressed in this e-mail
are those of the individual sender, except where specifically stated to be
those of an officer of Museum Victoria. Museum Victoria does not represent,
warrant or guarantee that the integrity of this communication has been
maintained nor that it is free from errors, virus or interference.
Museum Victoria
+61 3 8341
11 Nicholson St
Carlton
Victoria
www.museum.vic.gov.au
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Simon Blomberg, B.Sc.(Hons.), Ph.D, M.App.Stat.
Centre for Resource and Environmental Studies
The Australian National University
Canberra ACT 0200
Australia
T: +61 2 6125 7800 email: Simon.Blomberg_at_anu.edu.au
F: +61 2 6125 0757
CRICOS Provider # 00120C
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
