On May 17, 2011, at 4:09 AM, Bryony Tolhurst wrote:
Dear Bill
Many thanks. I will try this.
One question: why is the attach()function problematic? I have always
done it that way (well in my very limited R-using capacity!) as
dictated by 'Statistics, an Introduction using R' by Michael Crawley.
You should pick your dictators with more care.
My dataset is called Side ('Side.txt') so how do I import this data
without using attach(data)?
The data object is there as soon as you execute the read.table
function successfully.
I have tried:
side<-read.table('Side.txt',T)
# NOT attach(side)
The regression functions in R generally have a data argument, so you
would use this (as Bill already told you)
Model1 <- glm.nb(Cells ~ Cryogel*Day, data = side)
instead of:
data<-read.table('Side.txt',T)
attach(data)
But obviously I am still using the attach function, if not with
'data'!!
Right. There were two problems and you only addressed one of them.
--
David.
Thanks again
Bryony Tolhurst
-----Original Message-----
From: bill.venab...@csiro.au [mailto:bill.venab...@csiro.au]
Sent: 17 May 2011 03:21
To: Bryony Tolhurst; r-help@r-project.org
Subject: RE: [R] Post-hoc tests in MASS using glm.nb
?relevel
Also, you might want to fit the models as follows
Model1 <- glm.nb(Cells ~ Cryogel*Day, data = myData)
myData2 <- within(myData, Cryogel <- relevel(Cryogel, ref = "2"))
Model2 <- update(Model1, data = myData1)
&c
You should always spedify the data set when you fit a model if at
all possible. I would recommend you NEVER use attach() to put it on
the search path, (under all but the most exceptional circumstances).
You could fit your model as
Model0 <- glm.nv(Cells ~ interaction(Cryogel, Day) - 1, data = myData)
This will give you the subclass means as the regression
coefficients. You can then use vcov(Model0) to get the variance
matrix and compare any two you like using directly calculated t-
statistics. This is pretty straightforward as well.
Bill Venables.
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org
] On Behalf Of bryony
Sent: Tuesday, 17 May 2011 3:46 AM
To: r-help@r-project.org
Subject: [R] Post-hoc tests in MASS using glm.nb
I am struggling to generate p values for comparisons of levels (post-
hoc
tests) in a glm with a negative binomial distribution
I am trying to compare cell counts on different days as grown on
different media (e.g. types of cryogel) so I have 2 explanatory
variables (Day and Cryogel), which are both factors, and an over-
dispersed count variable (number of cells) as the response. I know
that both variables are significant, and that there is a significant
interaction between them.
However, I seem unable to generate multiple comparisons between the
days and cryogels.
So my model is
Model1<-glm.nb(Cells~Cryogel+Day+Day:Cryogel)
The output gives me comparisons between levels of the factors
relative to a reference level (Day 0 and Cryogel 1) as follows:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.2040 0.2743 4.389 1.14e-05 ***
Day14 3.3226 0.3440 9.658 < 2e-16 ***
Day28 3.3546 0.3440 9.752 < 2e-16 ***
Day7 3.3638 0.3440 9.779 < 2e-16 ***
Cryogel2 0.7097 0.3655 1.942 0.05215 .
Cryogel3 0.7259 0.3651 1.988 0.04677 *
Cryogel4 1.4191 0.3539 4.010 6.07e-05 ***
Day14:Cryogel2 -0.7910 0.4689 -1.687 0.09162 .
Day28:Cryogel2 -0.5272 0.4685 -1.125 0.26053
Day7:Cryogel2 -1.1794 0.4694 -2.512 0.01199 *
Day14:Cryogel3 -1.0833 0.4691 -2.309 0.02092 *
Day28:Cryogel3 0.1735 0.4733 0.367 0.71395
Day7:Cryogel3 -1.0907 0.4690 -2.326 0.02003 *
Day14:Cryogel4 -1.2834 0.4655 -2.757 0.00583 **
Day28:Cryogel4 -0.6300 0.4591 -1.372 0.16997
Day7:Cryogel4 -1.3436 0.4596 -2.923 0.00347 **
HOWEVER I want ALL the comparisons e.g. Cryogel 2 versus 4, 3 versus
2 etc on each of the days. I realise that such multiple comparsions
need to be approached with care to avoid Type 1 error, however it is
easy to do this in other programmes (e.g. SPSS, Genstat) and I'm
frustrated that it appears to be difficult in R. I have tried the
glht (multcomp) function but it gives me the same results. I assume
that there is some way of entering the data differently so as to
tell R to use a different reference level each time and re-run the
analysis for each level, but don't know what this is.
Please help!
Many thanks for your input
Bryony
--
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______________________________________________
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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