Hello everybody,
it may be better to have sample data. I have provided data with less
levels of "gene" and "day" and only ca. 400 data points per condition.
Sample code:
small=as.data.frame(read.csv("small.csv"))
small$species=factor(small$species)
small$gene=factor(small$gene)
small$day=factor(small$day)
small$repl=factor(small$repl)
library(lme4)
model1=lmer(APC~(FITC+species+gene)^3+(1|day)+(1|repl),REML=F,data=small)
model2=lmer(APC~(FITC+species+gene)^2+(1|day)+(1|repl),REML=F,data=small)
anova(model1,model2)
gives me a significant difference, but in fact there are far too many
residual df, and this is much worse in the original data. I suppose I
cannot trust this difference.
model3=lmer(APC~species*gene+(1|day/repl/FITC),data=small)
Error: length(f1) == length(f2) is not TRUE
In addition: Warning messages:
1: In FITC:(repl:day) :
numerical expression has 886 elements: only the first used
2: In FITC:(repl:day) :
numerical expression has 886 elements: only the first used
Can I do nesting without incurring this kind of error ?
And finally
model4=lmer(APC~gene*species+(1|day) + (1|repl) +
(1+(gene:species)|FITC),data=small)
model5=lmer(APC~gene+species+(1|day) + (1|repl) + (1|FITC),data=small)
anova(model4,model5)
works with this reduced data set, but fails to converge for the
original, much larger data set. I am unsure if this is the right kind of
analysis for the data and there is only a problem of convergence, or if
it is the wrong formula.
Can anybody give me some advice on this problem ? Or should I just stick
to reducing the data from each condition to a single parameter, as
explained in my first email below?
Thank you again!
Matthias Gralle wrote:
Hello everybody,
I have been trying for some weeks to state the correct design of my
experiment as a GLM formula, and have not been able to find something
appropriate in Pinheiro & Bates, so I am posting it here and hope
somebody can help me.
In each experimental condition, described by
1) gene (10 levels, fixed, because of high interest to me)
2) species (2 levels, fixed, because of high interest)
3) day (2 levels, random)
4) replicate (2 levels per day, random),
I have several thousand data points consisting of two variables:
5) FITC (level of transfection of a cell)
6) APC (antibody binding to the cell)
Because of intrinsic and uncontrollable cell-to-cell variation, FITC
varies quite uniformly over a wide range, and APC correlates rather
well with FITC. In some cases, I pasted day and replicate together as
day_repl.
My question is the following:
Is there any gene (in my set of 10 genes) where the species makes a
difference in the relation between FITC and APC ? If yes, in what gene
does species have an effect ? And what is the effect of the species
difference ?
My attempts are the following:
1. Fit the data points of each experimental condition to a linear
equation APC=Intercept+Slope*FITC and analyse the slopes :
lm(Slope~species*gene*day_repl)
This analysis shows clear differences between the genes, but no effect
of species and no interaction gene:species.
The linear fit to the cells is reasonably good, but of course does not
represent the data set completely, so I wanted to incorporate the
complete data set.
2a. lmer(APC~FITC*species*gene+(1|day)+(1|repl))
This gives extremely significant values for any interaction and
variable because there are >200 000 df. Of course, it cannot be true,
because the cells are not really independent. I have done many
variations of the above, e.g.
2b. lmer(APC~FITC*species*gene+(1|day)+(1+FITC|day_repl)),
but they all suffer from the excess of df.
3. lmer(APC~species*gene+(1|day/repl/FITC) gives several warning
messages like this one:
In repl:day :
numerical expression has 275591 elements: only the first used
4. lmer(APC~gene*species+(1|day_repl) + (1+gene:species|FITC)) ran
several days, but failed to converge...
Can somebody give me any hint, or do you think the only possible
analysis is a simplification as in my model 1 ?
By the way, I am using R version 2.8.0 (2008-10-20) on Ubuntu 8.04 on
a linux 2.6.24-24-generic kernel on different Intel systems. I am
using the lme4 that came with R 2.8.0.
Thank you very much for your time!
-- Matthias Gralle, PhD
Dept. Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig, Germany
Tel +49 341 3550 519
Fax +49 341 3550 555
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Matthias Gralle, PhD
Dept. Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig, Germany
Tel +49 341 3550 519
Fax +49 341 3550 555
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
