modelling and R misconceptions; was: package installtion

My responses are in brackets below, plus a final note after the main text.

----- Original Message -----
From: Uwe Ligges <ligges at statistik.tu-dortmund.de>
To: Scott Raynaud <scott.raynaud at yahoo.com>
Cc: "r-help at r-project.org" <r-help at r-project.org>
Sent: Thursday, November 17, 2011 9:16 AM
Subject: Re: [R] modelling and R misconceptions; was: package installtion

This is hopeless [That's a matter of perception-even concentration camp prisoners 
found a way to hope (see Viktor Frankl)], since you never [never is a strong word 
and many times leads to cognitive errors] seem to listen to our 
advice [It's possible that I misunderstood your recommedations (more likely), 
or that you communicated poorly (less likely)], 
therefore this will be my very last try:

So you actually need local advice [Yes I need advice-that's why I post here!], 
both for statistical concepts and R related?[I don't claim to be a statistical genius, 
but I can hold my own.? Now, R is a different matter].? No statistics software 
can estimate effects of variables that you observed to be constant (e.g. 0) 
all the time [I think you misuderstood my intentions-I never wanted to estimate 
effects that are 0 all of the time]. If any software does, 
please delete it a once from your machine.
Instead, ask a local statistician for advice on your problem. You 
certainly want to show the data and your model to the local expert - 
since you don't show us. [I gave a detailed explanation in a previous post which I repeat here:

|OK, I'm using William Browne's MLPowSim to create an R script which will simulate samples for estimation of sample size in mixed models.? I have subjects
|?nested in hospitals with hospitals treated as random and all of my covariates at level 1.? My outcome is death, so it's binary and I'll have a fixed and 
|random intercept.? My interest is in the relation of the covariates to the outcome.? 
|?
|My most important variable is gestational age (GA) which my investigators divide thusly: 23-24, 25-26, 27-28, 29-30 and 31-32.? I have recoded the
|?dummies for GA in the script according to the MLPowSim instructions to a random multinomial variable:
|?
|?????????????? macpred<-rmultinom(n2,1,c(.1031,.1482,.2385,.4404,.0698)) 
|?????????????? x[,3]<-macpred[1,][l2id]
|?????????????? x[,4]<-macpred[2,][l2id]
|?????????????? x[,5]<-macpred[3,][l2id]
|?????????????? x[,6]<-macpred[4,][l2id]
|
|GA 23-24 is the reference with p=.0698.? I started with a structured sampling scheme of 20, 60, 100, 120 and 140 level 2 units.? My level 2 units have 
|different sizes.? So at 20 I had 5 hospitals with 100 patients, 4 with 280, 3 with 460, 3 with 640, 3 with 820 and 2 with 1000.? Thus, at 60 hospitals, I have 15, 
|12, 9, 9, 9, 6 with the same cell sample sizes.
|?
|According to the MLPowSim documentation, with small probablities it's possible to have a column of zeroes in the X matrix if there are not many units in 
|the random factor.? R will choke on this but MLWin sets the associated fixed effects to 0.? When R?choked, I increased from 20 to 60 as my minimum as 
|suggested in the MLPowSim documentation.? Still no luck.

Since this is a simulation, I assume once and a while that by chance a coefficient could be 0. 
In fact, Browne mentions as much in his documentation.? There is a bit more to my simulation, 
but I thought I'd try to keep it as simple as possible, at least at the outset.]

And then you want to ask for local R course 
since reading the documentation seems not to help [You got that right!]. Applying mtrace() in 
a non exiting object shows this straight away.

Uwe Ligges

Apparently I misuderstood the prupose of mtrace after reading the documentation-I thought it was 
to debug problems of the sort I've encountered.? Michael Weylandt provided appropriate direction 
in the previous post for which I am grateful.

Not all of us can be intellectual superstars.? That's why we ask for help.? This much I did read and understand
from the R posting guide:

Responding to other posts: 
	* Rudeness and ad hominem comments are not acceptable. Brevity is OK. 
It's a good lesson to learn.

modelling and R misconceptions; was: package installtion

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