Dear Bryony: the suggestion was not to change the name of the data object, but
to explicitly tell glm.nb what dataset it should look in to find the variables
you mention in the formula.
so the salient difference is:
m1 <- glm.nb(Cells ~ Cryogel*Day, data = side)
instead of
attach(side)
m1 <- glm.nb(Cells ~ Cryogel*Day)
This works for other functions also, but not uniformly as yet (how I wish it
did and I could say
hist(x, data=side)
Instead of
hist(side$x)
this inconsistency encourages the need for attach()
best, tim
On 17 May 2011, at 9:09 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. My dataset is
> called Side ('Side.txt') so how do I import this data without using
> attach(data)? I have tried:
>
> side<-read.table('Side.txt',T)
> attach(side)
>
> instead of:
>
> data<-read.table('Side.txt',T)
> attach(data)
>
> But obviously I am still using the attach function, if not with 'data'!!
>
> 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
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Post-hoc-tests-in-MASS-using-glm-nb-tp3526934p3526934.html
> Sent from the R help mailing list archive at Nabble.com.
>
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> ______________________________________________
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