R help - Apr 2003 - Basic LME

Hello R Users,
I am investigating the basic use of the LME function, using the following
example;

Response is Weight, covariate is Age, random factor is Genotype

model.lme <- lme (Weight~Age, random=~ 1|Genotype)

After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758.

I am comparing the above model with the AOV function;

model.aov <- aov (Weight~Age + Genotype)

I find that the estimate of Age is also 0.098, and p=0.758 as in the LME model
above.

So, my questions are;

1: I expected that the LME model would be a better way to analyse this data
compared to the AOV model, since Genotype is a random factor. However, I obtain
the same parameter estimate and p value for Age. Please can someone tell me why?

2: When using LME, when I am after a p value for the covariate Age, is it better
to do the following;

Model.lme2 <- lme (Weight~Age, random=~ 1|Genotype, method="ML")
Model.lme3 <- lme (Weight~1, random=~ 1|Genotype, method="ML")
Anova(Model.lme2, Model.lme3)

Giving likelihood ratio=0.102, with p=0.749, which is slightly different to the
p values of 0.758 above.


Thanks for your attention,
Martin.


Martin Hoyle,
School of Life and Environmental Sciences,
University of Nottingham,
University Park,
Nottingham,
NG7 2RD,
UK
Webpage: http://myprofile.cos.com/martinhoyle

I'm certainly no expert, but I believe that aov( ) is simply a
front-end to the lm( ) function, which fits fixed-effects linear
models.  As you may know, it is possible to write any ANOVA model
as a regression model with appropriate use of indicator variables
for the various levels of treatments.

So, aov( ) does not, in general, yield results that are comparable
to those generated by lme( ).  You have specified the same _fixed_
effect ( ~ Age ) in both aov( ) and lme( ) which is why they
give you the same parameter estimates for this covariate.

When you perform anova( ) to compare the two lme( ) models, you
are performing a likelihood ratio test.  This is not generally
the same as the F-test/t-test used to evaluate the marginal 
significance of a fixed-effect term in a linear model, which explains 
the difference between the p-values.


Best,
 
  david paul

-----Original Message-----
From: Martin Hoyle [mailto:plxmh at nottingham.ac.uk] 
Sent: Tuesday, April 08, 2003 10:09 AM
To: r-help at stat.math.ethz.ch
Subject: [R] Basic LME


Hello R Users,
I am investigating the basic use of the LME function, using the following
example;

Response is Weight, covariate is Age, random factor is Genotype

model.lme <- lme (Weight~Age, random=~ 1|Genotype)

After summary(model.lme), I find that the estimate of Age is 0.098 with
p=0.758.

I am comparing the above model with the AOV function;

model.aov <- aov (Weight~Age + Genotype)

I find that the estimate of Age is also 0.098, and p=0.758 as in the LME
model above.

So, my questions are;

1: I expected that the LME model would be a better way to analyse this data
compared to the AOV model, since Genotype is a random factor. However, I
obtain the same parameter estimate and p value for Age. Please can someone
tell me why?

2: When using LME, when I am after a p value for the covariate Age, is it
better to do the following;

Model.lme2 <- lme (Weight~Age, random=~ 1|Genotype, method="ML")
Model.lme3
<- lme (Weight~1, random=~ 1|Genotype, method="ML")
Anova(Model.lme2,
Model.lme3)

Giving likelihood ratio=0.102, with p=0.749, which is slightly different to
the p values of 0.758 above.


Thanks for your attention,
Martin.


Martin Hoyle,
School of Life and Environmental Sciences,
University of Nottingham,
University Park,
Nottingham,
NG7 2RD,
UK
Webpage: http://myprofile.cos.com/martinhoyle

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Martin Hoyle <plxmh at nottingham.ac.uk> said:
> Hello R Users,
> I am investigating the basic use of the LME function, using the following 
example;
> 
> Response is Weight, covariate is Age, random factor is Genotype
> 
> model.lme <- lme (Weight~Age, random=~ 1|Genotype)
> 
> After summary(model.lme), I find that the estimate of Age is 0.098 with 
p=0.758.
> 
> I am comparing the above model with the AOV function;
> 
> model.aov <- aov (Weight~Age + Genotype)
> 
> I find that the estimate of Age is also 0.098, and p=0.758 as in the LME 
model above.

Have a look at summary(model.lme), find the "Random Effect:"-part and
see
whether the Genotype variation is close to 0. If so, this would explain why 
the estimates are similar, as the random factor Genotype then has almost 
negligible variation. 

Pinheiro, J. C. and Bates, D. M. (2000): "Mixed-Effects Models in S and S-
PLUS" discuss the merits of the different tests.

Christian