Hi all,
does anyone know how to automatically update starting values in R?
I' m fitting multiple nonlinear models and would like to know how I can update starting values without having to type them in.
thank all


--- On Fri, 12/26/08, r-help-request@r-project.org <r-help-request@r-project.org> wrote:

From: r-help-request@r-project.org <r-help-request@r-project.org>
Subject: R-help Digest, Vol 70, Issue 26
To: r-help@r-project.org
Date: Friday, December 26, 2008, 6:00 AM

Send R-help mailing list submissions to
	r-help@r-project.org

To subscribe or unsubscribe via the World Wide Web, visit
	https://stat.ethz.ch/mailman/listinfo/r-help
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When replying, please edit your Subject line so it is more specific
than "Re: Contents of R-help digest..."


Today's Topics:

   1. Re: Implementing a linear restriction in lm() (Ravi Varadhan)
   2. p(H0|data) for lm/lmer-objects R (Leo G?rtler)
   3. Re: beginner data.frame question (Oliver Bandel)
   4. Re: 4 questions regarding hypothesis testing, survey package,
      ts on samples, plotting (Thomas Lumley)
   5. Re: How can I avoid nested 'for' loops or quicken the
      process? (Oliver Bandel)
   6. Re: 4 questions regarding hypothesis testing, survey package,
      ts on samples, plotting (Peter Dalgaard)
   7. Re: 4 questions regarding hypothesis testing, survey package,
      ts on samples, plotting (Ben Bolker)
   8. Re: Class and object problem (Ben Bolker)
   9. Re: 4 questions regarding hypothesis testing, survey package,
      ts on samples, plotting (Peter Dalgaard)
  10. Re: p(H0|data) for lm/lmer-objects R (Daniel Malter)
  11. Re: Implementing a linear restriction in lm() (Daniel Malter)
  12. Re: p(H0|data) for lm/lmer-objects R (Andrew Robinson)
  13.  Percent damage distribution (diegol)
  14. Re: ggplot2 Xlim (Wayne F)
  15. Re: creating standard curves for ELISA analysis (1Rnwb)
  16. Re: Percent damage distribution (Ben Bolker)
  17. Re: How can I avoid nested 'for' loops or quicken the
      process? (Prof Brian Ripley)
  18. Re: Percent damage distribution (Prof Brian Ripley)
  19. Upgrading R causes Tinn-R to freeze. (rkevinburton@charter.net)


----------------------------------------------------------------------

Message: 1
Date: Thu, 25 Dec 2008 11:39:33 -0500
From: Ravi Varadhan <rvaradhan@jhmi.edu>
Subject: Re: [R] Implementing a linear restriction in lm()
To: Serguei Kaniovski <Serguei.Kaniovski@wifo.ac.at>
Cc: r-help@stat.math.ethz.ch
Message-ID: <f5bef5b03d6.495370f5@johnshopkins.edu>
Content-Type: text/plain; charset=iso-8859-1

Hi,

You could use the "offset" argument in lm().  Here is an example:

set.seed(123)
x <- runif(50)
beta <- 1
y <- 2 + beta*x + rnorm(50)

model1 <- lm (y ~ x)
model2 <- lm (y ~ 1, offset=x)

anova(model2, model1)

Best,
Ravi.
____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan@jhmi.edu


----- Original Message -----
From: Serguei Kaniovski <Serguei.Kaniovski@wifo.ac.at>
Date: Wednesday, December 24, 2008 9:39 pm
Subject: [R] Implementing a linear restriction in lm()
To: r-help@stat.math.ethz.ch


>  
>  Dear All!
>  
>  I want to test a coeffcient restriction beta=1 in a univariate model 
> lm
>  (y~x). Entering
>  lm((y-x)~1) does not help since anova test requires the same dependent
>  variable. What is the right way to proceed?
>  
>  Thank you for your help and marry xmas,
>  Serguei Kaniovski
>  ________________________________________
>  Austrian?Institute?of?Economic?Research?(WIFO)
>  
>  P.O.Box?91??????????????????????????Tel.:?+43-1-7982601-231
>  1103?Vienna,?Austria????????Fax:?+43-1-7989386
>  
>  Mail:?Serguei.Kaniovski@wifo.ac.at
>  
>  	[[alternative HTML version deleted]]
>   
> ______________________________________________
>  R-help@r-project.org mailing list
>  
>  PLEASE do read the posting guide 
>  and provide commented, minimal, self-contained, reproducible code.



------------------------------

Message: 2
Date: Thu, 25 Dec 2008 19:51:36 +0100
From: Leo G?rtler <leog@anicca-vijja.de>
Subject: [R] p(H0|data) for lm/lmer-objects R
To: r-help@stat.math.ethz.ch
Message-ID: <4953D638.4090507@anicca-vijja.de>
Content-Type: text/plain; charset=ISO-8859-15

Dear R-List,

I am interested in the Bayesian view on parameter estimation for
multilevel models and ordinary regression models. AFAIU traditional
frequentist p-values they give information about p(data_or_extreme|H0).
AFAIU it further, p-values in the Fisherian sense are also no alpha/type
 I errors and therefor give no information about future replications.

However, p(data_or_extreme|H0) is not really interesting for social
science research questions (psychology). Much more interesting is
p(H0|data). Is there a way or formula to calculate these probabilities
of the H0 (or another hypothesis) from lm-/lmer objects in R?

Yes I know that multi-level modeling as well as regression can be done
in a purely Bayesian way. However, I am not capable of Bayesian
statistics, therefor I ask that question. I am starting to learn it a
little bit.

The frequentist literature - of course - does not cover that topic.

Thanks a lot,
best,

leo g?rtler



------------------------------

Message: 3
Date: Thu, 25 Dec 2008 19:49:58 +0000 (UTC)
From: Oliver Bandel <oliver@first.in-berlin.de>
Subject: Re: [R] beginner data.frame question
To: r-help@stat.math.ethz.ch
Message-ID: <loom.20081225T193443-238@post.gmane.org>
Content-Type: text/plain; charset=us-ascii

John Fox <jfox <at> mcmaster.ca> writes:

> 
> Dear Kirk,
> 
> Actually, co2 isn't a data frame but rather a "ts"
(timeseries) object. A
> nice thing about R is that you can query and examine objects:
> 
> > class(co2)
> [1] "ts"
[...]

Yes.

And with 


> frequency(co2)
[1] 12

One gets "the number of observations per unit of time".

When one sets the parameter "start" and "frequency" or
"start" and "deltat" or
"start" and "end" of a time-series, one can set the used
values and that means
that functions that use those values, also will be controlled by this.

> start(co2)
[1] 1959    1
> end(co2)
[1] 1997   12


Rearranging by creaating a new ts-object with different
timely parameters:

> new_co2 <- ( ts( co2,frequency=1, start=1959) )
> start(new_co2)
[1] 1959    1
> end(new_co2)
[1] 2426    1


.... and the way back:

> old_co2 <- ( ts( new_co2, frequency=12, start=1959) )
> start(old_co2)
[1] 1959    1
> end(old_co2)
[1] 1997   12
> 


using plot on those values will result in different plots.


Why the mentioned test data is showed different wih summary,
[[elided Yahoo spam]]


To have the test-data (or the way how it was constructed)
could help in helping.

Ciao,
   Oliver



------------------------------

Message: 4
Date: Thu, 25 Dec 2008 12:00:21 -0800 (PST)
From: Thomas Lumley <tlumley@u.washington.edu>
Subject: Re: [R] 4 questions regarding hypothesis testing, survey
	package, ts on samples, plotting
To: Peter Dalgaard <p.dalgaard@biostat.ku.dk>
Cc: r-help@r-project.org, Ben Bolker <bolker@ufl.edu>
Message-ID:
	<Pine.LNX.4.43.0812251200210.24756@hymn11.u.washington.edu>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed

On Wed, 24 Dec 2008, Peter Dalgaard wrote:

> Ben Bolker wrote:
>> 
>> 
>> Khawaja, Aman wrote:
>>> I need to answer one of the question in my open source test is:
What are
>>> the four questions asked about the parameters in hypothesis
testing?
>>> 
>>> 
>> 
>> Please check the posting guide.
>> * We don't answer homework questions ("open source"
doesn't mean
>> that other people answer the questions for you, it means you can find
>> the answers outside your own head -- and in any case, we don't
have
>> any of way of knowing that the test is really open).
>> * this is not an R question but a statistics question
>> * please don't post the same question multiple times
>
>
> Besides, this is really unanswerable without access to your teaching
material, 
> which probably has a list of four questions somewhere...

Starting with 'Why is this parameter different from all other
parameters?', perhaps.

> It is a bit like the History question: "Who was what in what of
whom?"
>
>

A traditional British equivalent is "Who dragged whom how many times
around the walls of where?", which does have just about enough context.

The R answer to the original post would probably be

1. Why aren't there any p-values in lmer()?
2. How do I extract p-values from lm()?
3. Can R do post-hoc tests?
4. Can R do tests of normality?

and in statistical consulting the questions might be

1. Doesn't that assume a Normal distribution?
2. Do you have a reference for that?
3. What was the power for that test?
4. Can you redo the test just in the left-handed avocado farmers[*]


         -thomas



[*] this particular subset (c) joel on software.

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley@u.washington.edu	University of Washington, Seattle



------------------------------

Message: 5
Date: Thu, 25 Dec 2008 20:20:48 +0000 (UTC)
From: Oliver Bandel <oliver@first.in-berlin.de>
Subject: Re: [R] How can I avoid nested 'for' loops or quicken the
	process?
To: r-help@stat.math.ethz.ch
Message-ID: <loom.20081225T201648-168@post.gmane.org>
Content-Type: text/plain; charset=us-ascii

Bert Gunter <gunter.berton <at> gene.com> writes:

> 
> FWIW:
> 
> Good advice below! -- after all, the first rule of optimizing code is:
> Don't!
> 
> For the record (yet again), the apply() family of functions (and their
> packaged derivatives, of course) are "merely" vary carefully
written for()
> loops: their main advantage is in code readability, not in efficiency
gains,
> which may well be small or nonexistent. True efficiency gains require
> "vectorization", which essentially moves the for() loops from
interpreted
> code to (underlying) C code (on the underlying data structures): e.g.
> compare rowMeans() [vectorized] with ave() or apply(..,1,mean).
[...]

The apply-functions do bring speed-advantages.

This is not only what I read about it,
I have used the apply-functions and really got
results faster.

The reason is simple: an apply-function does
make in C, what otherwise would be done on the level of R
with for-loops.

Ciao,
   Oliver



------------------------------

Message: 6
Date: Thu, 25 Dec 2008 21:25:58 +0100
From: Peter Dalgaard <p.dalgaard@biostat.ku.dk>
Subject: Re: [R] 4 questions regarding hypothesis testing, survey
	package, ts on samples, plotting
To: Thomas Lumley <tlumley@u.washington.edu>
Cc: r-help@r-project.org, Ben Bolker <bolker@ufl.edu>
Message-ID: <4953EC56.9010808@biostat.ku.dk>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Thomas Lumley wrote:
> On Wed, 24 Dec 2008, Peter Dalgaard wrote:

>> It is a bit like the History question: "Who was what in what of
whom?"
>>
>>
> 
> A traditional British equivalent is "Who dragged whom how many times 
> around the walls of where?", which does have just about enough
context.

Yes. "Joshua, Isrelites, seven, Jericho" is wrong by a hair....

-- 
    O__  ---- Peter Dalgaard             ?ster Farimagsgade 5, Entr.B
   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk)              FAX: (+45) 35327907



------------------------------

Message: 7
Date: Thu, 25 Dec 2008 15:30:10 -0500
From: Ben Bolker <bolker@ufl.edu>
Subject: Re: [R] 4 questions regarding hypothesis testing, survey
	package, ts on samples, plotting
To: Peter Dalgaard <p.dalgaard@biostat.ku.dk>
Cc: r-help@r-project.org, Thomas Lumley <tlumley@u.washington.edu>
Message-ID: <4953ED52.30801@ufl.edu>
Content-Type: text/plain; charset=ISO-8859-1

Peter Dalgaard wrote:
> Thomas Lumley wrote:
>> On Wed, 24 Dec 2008, Peter Dalgaard wrote:
> 
>>> It is a bit like the History question: "Who was what in what
of whom?"
>>>
>>>
>>
>> A traditional British equivalent is "Who dragged whom how many
times
>> around the walls of where?", which does have just about enough
context.
> 
> Yes. "Joshua, Isrelites, seven, Jericho" is wrong by a hair....
> 

  Hmmm.  Achilles, Hector, ?, Troy.

http://en.wikipedia.org/wiki/Achilles:

Achilles chased Hector around the wall of Troy three times before
Athena, in the form of Hector's favorite and dearest brother, Deiphobus,
persuaded Hector to stop running and fight Achilles face to face. After
Hector realized the trick, he knew his death was inevitable and accepted
his fate. Hector, wanting to go down fighting, charged at Achilles with
his only weapon, his sword. Achilles got his vengeance, killing Hector
with a single blow to the neck. He then tied Hector's body to his
chariot and dragged it around the battlefield for nine days.

-- 
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker@ufl.edu / www.zoology.ufl.edu/bolker
GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc



------------------------------

Message: 8
Date: Thu, 25 Dec 2008 21:17:56 +0000 (UTC)
From: Ben Bolker <bolker@ufl.edu>
Subject: Re: [R] Class and object problem
To: r-help@stat.math.ethz.ch
Message-ID: <loom.20081225T211712-883@post.gmane.org>
Content-Type: text/plain; charset=us-ascii

Odette Gaston <odette.gaston <at> gmail.com> writes:

> 
> Dear all,
> 
> I have a problem with accessing class attributes. 
> I was unable to solve this
> yet, but someone may know how to solve it.

My best guess at your immediate problem (doing
things by hand) is that you're not using the
whole vector.  From your example:

Delta <- c(m1 = 0, m2 = 1.8, m3 = 4.2, m4 = 6.2)
exp(-0.5*Delta)/sum(exp(-0.5*Delta))
        m1         m2         m3         m4 
0.63529363 0.25829111 0.07779579 0.02861947 

   In general the dRedging package at
http://www.zbs.bialowieza.pl/users/kamil/r/ can do these
problems (I hate to recommend this package because it
offers the danger of thoughtless convenience,
but if you really know that you want to enumerate
models and do IC-based model averaging it can save a
lot of time).  At the moment, though, it doesn't work
with glmmML-based objects (you could ask the author
to extend it).

  When I tried stepAIC it didn't really enumerate
all the models for me (that's not its purpose),
so I went through and enumerated by hand.  For example;

library(glmmML)
set.seed(1001)
a <- runif(100)
b <- runif(100)
c <- runif(100)
x <- runif(100)
n <- rep(20,100)
cluster <- factor(rep(1:5,20))
linpred <- a+b+c+x-2
y <- rbinom(100,prob=plogis(linpred),size=n)
data <- data.frame(y,a,b,c,x,n)

m <- list()
## full model
m[[1]] <- glmmML(cbind (y, n-y)~ x+a+b+c, 
    family = binomial, data, cluster)
## 3-term models
m[[2]] <- update(m[[1]],.~.-a) ## xbc
m[[3]] <- update(m[[1]],.~.-b) ## xac
m[[4]] <- update(m[[1]],.~.-c) ## xab
m[[5]] <- update(m[[1]],.~.-x) ## abc
## 2-term models
m[[6]] <- update(m[[2]],.~.-x) ## bc
m[[7]] <- update(m[[2]],.~.-b) ## xc
m[[8]] <- update(m[[2]],.~.-c) ## xb
m[[9]] <- update(m[[3]],.~.-x) ## ac
m[[10]] <- update(m[[3]],.~.-c) ## xa
m[[11]] <- update(m[[4]],.~.-x) ## ab
## 0-term models (intercept)
m[[12]] <- glmmML(cbind (y, n-y)~ 1, family = binomial, data, cluster)
m[[13]] <- update(m[[12]],.~.+a)
m[[14]] <- update(m[[12]],.~.+b)
m[[15]] <- update(m[[12]],.~.+c)
m[[16]] <- update(m[[12]],.~.+x)

## have to define logLik and AIC for glmmML objects
logLik.glmmML <- function(x) {
  loglik <- (-x$deviance)/2
  attr(loglik,"df") <- length(coef(x))
  loglik
}
AIC.glmmML <- function(x) x$aic

library(bbmle)
## now it works (the answers are pretty trivial
## in this made-up case
AICtab(m,sort=TRUE,weights=TRUE,delta=TRUE)



------------------------------

Message: 9
Date: Thu, 25 Dec 2008 22:20:38 +0100
From: Peter Dalgaard <p.dalgaard@biostat.ku.dk>
Subject: Re: [R] 4 questions regarding hypothesis testing, survey
	package, ts on samples, plotting
To: Ben Bolker <bolker@ufl.edu>
Cc: r-help@r-project.org, Thomas Lumley <tlumley@u.washington.edu>
Message-ID: <4953F926.5040603@biostat.ku.dk>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Ben Bolker wrote:
> Peter Dalgaard wrote:
>> Thomas Lumley wrote:
>>> On Wed, 24 Dec 2008, Peter Dalgaard wrote:
>>>> It is a bit like the History question: "Who was what in
what of whom?"
>>>>
>>>>
>>> A traditional British equivalent is "Who dragged whom how
many times
>>> around the walls of where?", which does have just about
enough context.
>> Yes. "Joshua, Isrelites, seven, Jericho" is wrong by a
hair....
>>
> 
>   Hmmm.  Achilles, Hector, ?, Troy.
> 
> http://en.wikipedia.org/wiki/Achilles:
> 
> Achilles chased Hector around the wall of Troy three times before
> Athena, in the form of Hector's favorite and dearest brother,
Deiphobus,
> persuaded Hector to stop running and fight Achilles face to face. After
> Hector realized the trick, he knew his death was inevitable and accepted
> his fate. Hector, wanting to go down fighting, charged at Achilles with
> his only weapon, his sword. Achilles got his vengeance, killing Hector
> with a single blow to the neck. He then tied Hector's body to his
> chariot and dragged it around the battlefield for nine days.
> 

I have

http://thanasis.com/achilles.htm

Achilles ignored Hector's dying wish to have his body returned to his 
father Priam for ransom. Instead he fastened leather straps to the body 
of Hector, secured them on his chariot and whipping up his immortal 
horses Balius, Xanthus and Pedasus, dragged the corpse three times 
around the walls of Troy, much to the dismay of the devastated Trojans.



-- 
    O__  ---- Peter Dalgaard             ?ster Farimagsgade 5, Entr.B
   c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk)              FAX: (+45) 35327907



------------------------------

Message: 10
Date: Thu, 25 Dec 2008 16:35:35 -0500
From: "Daniel Malter" <daniel@umd.edu>
Subject: Re: [R] p(H0|data) for lm/lmer-objects R
To: " 'Leo G?rtler' " <leog@anicca-vijja.de>,
	<r-help@stat.math.ethz.ch>
Message-ID: <200812252135.AHJ65697@md4.mail.umd.edu>
Content-Type: text/plain;	charset="iso-8859-1"

This is very opaque to me. But if H0 is a null hypothesis (i.e. a hypothesis
about one or several coefficients in your model), then you can test linear
or nonlinear restrictions of the coefficients. Because your coefficients are
derived using your data, it appears to me you get something like a
p(H0|data).


-------------------------
cuncta stricte discussurus
-------------------------

-----Urspr?ngliche Nachricht-----
Von: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org] Im
Auftrag von Leo G?rtler
Gesendet: Thursday, December 25, 2008 1:52 PM
An: r-help@stat.math.ethz.ch
Betreff: [R] p(H0|data) for lm/lmer-objects R

Dear R-List,

I am interested in the Bayesian view on parameter estimation for multilevel
models and ordinary regression models. AFAIU traditional frequentist
p-values they give information about p(data_or_extreme|H0).
AFAIU it further, p-values in the Fisherian sense are also no alpha/type  I
errors and therefor give no information about future replications.

However, p(data_or_extreme|H0) is not really interesting for social science
research questions (psychology). Much more interesting is p(H0|data). Is
there a way or formula to calculate these probabilities of the H0 (or
another hypothesis) from lm-/lmer objects in R?

Yes I know that multi-level modeling as well as regression can be done in a
purely Bayesian way. However, I am not capable of Bayesian statistics,
therefor I ask that question. I am starting to learn it a little bit.

The frequentist literature - of course - does not cover that topic.

Thanks a lot,
best,

leo g?rtler

______________________________________________
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.



------------------------------

Message: 11
Date: Thu, 25 Dec 2008 16:42:27 -0500
From: "Daniel Malter" <daniel@umd.edu>
Subject: Re: [R] Implementing a linear restriction in lm()
To: "'Serguei Kaniovski'"
<Serguei.Kaniovski@wifo.ac.at>
Cc: r-help@stat.math.ethz.ch
Message-ID: <200812252142.DMB84001@md0.mail.umd.edu>
Content-Type: text/plain;	charset="iso-8859-1"

 
If it is only for a single coefficient you can just subtract your test-value
from the coefficient and divide by the coefficient's standard-error, which
gives you a t-value for the test (see Greene 2006). 

Otherwise, lookup "linear.hypothesis" in the "car" library.

Cheers,
Daniel

-------------------------
cuncta stricte discussurus
-------------------------

-----Urspr?ngliche Nachricht-----
Von: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org] Im
Auftrag von Ravi Varadhan
Gesendet: Thursday, December 25, 2008 11:40 AM
An: Serguei Kaniovski
Cc: r-help@stat.math.ethz.ch
Betreff: Re: [R] Implementing a linear restriction in lm()

Hi,

You could use the "offset" argument in lm().  Here is an example:

set.seed(123)
x <- runif(50)
beta <- 1
y <- 2 + beta*x + rnorm(50)

model1 <- lm (y ~ x)
model2 <- lm (y ~ 1, offset=x)

anova(model2, model1)

Best,
Ravi.
____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology School of Medicine Johns
Hopkins University

Ph. (410) 502-2619
email: rvaradhan@jhmi.edu


----- Original Message -----
From: Serguei Kaniovski <Serguei.Kaniovski@wifo.ac.at>
Date: Wednesday, December 24, 2008 9:39 pm
Subject: [R] Implementing a linear restriction in lm()
To: r-help@stat.math.ethz.ch


>  
>  Dear All!
>  
>  I want to test a coeffcient restriction beta=1 in a univariate model 
> lm  (y~x). Entering
>  lm((y-x)~1) does not help since anova test requires the same 
> dependent  variable. What is the right way to proceed?
>  
>  Thank you for your help and marry xmas,  Serguei Kaniovski  
> ________________________________________
>  Austrian?Institute?of?Economic?Research?(WIFO)
>  
>  P.O.Box?91??????????????????????????Tel.:?+43-1-7982601-231
>  1103?Vienna,?Austria????????Fax:?+43-1-7989386
>  
>  Mail:?Serguei.Kaniovski@wifo.ac.at
>  
>  	[[alternative HTML version deleted]]
>   
> ______________________________________________
>  R-help@r-project.org mailing list
>  
>  PLEASE do read the posting guide
>  and provide commented, minimal, self-contained, reproducible code.

______________________________________________
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.



------------------------------

Message: 12
Date: Fri, 26 Dec 2008 09:03:54 +1100
From: Andrew Robinson <A.Robinson@ms.unimelb.edu.au>
Subject: Re: [R] p(H0|data) for lm/lmer-objects R
To: "'Leo G?rtler'" <leog@anicca-vijja.de>
Cc: r-help@stat.math.ethz.ch
Message-ID: <20081225220354.GH1294@ms.unimelb.edu.au>
Content-Type: text/plain; charset=us-ascii

Dear Leo,

> Dear R-List,
> 
> I am interested in the Bayesian view on parameter estimation for
multilevel
> models and ordinary regression models. 

You might find Gelman & Hill's recent book to be good reading, and
there is a book in the Use-R series that focuses on using R to perform
Bayesian analyses.  

> AFAIU traditional frequentist p-values they give information about
> p(data_or_extreme|H0).  AFAIU it further, p-values in the Fisherian
> sense are also no alpha/type I errors and therefor give no
> information about future replications.

I don't think that the last comment is necessarily relevant nor is it
necessarily true.

> However, p(data_or_extreme|H0) is not really interesting for social
science
> research questions (psychology). Much more interesting is
> p(H0|data). 

That's fine, but first you have to believe that the statement has
meaning.

> Is there a way or formula to calculate these probabilities of the H0
> (or another hypothesis) from lm-/lmer objects in R?

See the books above.  Note that in order to do so, you will need to
nominate a prior distribution of some kind.

> Yes I know that multi-level modeling as well as regression can be done in
a
> purely Bayesian way. However, I am not capable of Bayesian statistics,
> therefor I ask that question. I am starting to learn it a little bit.

No offense, but it sounds to me like you want to have the Bayesian
omelette without breaking the Bayesian eggs ;).  Certain kinds of
multi-level models are mathematically identical to certain kinds of
Empirical Bayes models, but that does not make them Bayesian (despite
what some people say).  I caution against your implied goal of
obtaining Bayesian statistics without performing a Bayesian analysis.
 
Good luck,

Andrew

-- 
Andrew Robinson  
Department of Mathematics and Statistics            Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia         Fax: +61-3-8344-4599
http://www.ms.unimelb.edu.au/~andrewpr
http://blogs.mbs.edu/fishing-in-the-bay/



------------------------------

Message: 13
Date: Thu, 25 Dec 2008 14:20:29 -0800 (PST)
From: diegol <diegol81@gmail.com>
Subject: [R]  Percent damage distribution
To: r-help@r-project.org
Message-ID: <21170344.post@talk.nabble.com>
Content-Type: text/plain; charset=UTF-8


R version: 2.7.0
Running on: WinXP

I am trying to model damage from fire losses (given that the loss occurred).
Since I have the individual insured amounts, rather than sampling dollar
damage from a continuous distribution ranging from 0 to infinity, I want to
sample from a percent damage distribution from 0-100%. One obvious solution
is to use runif(n, min=0, max=1), but this does not seem to be a good idea,
since I would not expect damage to be uniform.

I have not seen such a distribution in actuarial applications, and rather
than inventing one from scratch I thought I'd ask you if you know one,
maybe
from other disciplines, readily available in R.

Thank you in advance.

-----
~~~~~~~~~~~~~~~~~~~~~~~~~~
Diego Mazzeo
Actuarial Science Student
Facultad de Ciencias Econ?micas
Universidad de Buenos Aires
Buenos Aires, Argentina
-- 
View this message in context:
http://www.nabble.com/Percent-damage-distribution-tp21170344p21170344.html
Sent from the R help mailing list archive at Nabble.com.



------------------------------

Message: 14
Date: Thu, 25 Dec 2008 14:43:45 -0800 (PST)
From: Wayne F <wdf61@mac.com>
Subject: Re: [R] ggplot2 Xlim
To: r-help@r-project.org
Message-ID: <21170453.post@talk.nabble.com>
Content-Type: text/plain; charset=us-ascii


I'm just a ggplot2 beginner, but...

It seems to me that you're mixing continuous and factor variables/concepts.
It looks to me as if ForkLength and Number are continuous values. But
you'll
need to convert ForkLength into a factor before using geom="bar". I
do that
and the graph "works" but the bars are extremely busy, which I assume
is
what you mean by "crowded".

As I try several different things, I'm seeing error messages. Are you not
seeing error messages?

Is the bottom line that you simply want to display some continuous data in a
histogram-ish style, and you don't like the default "binning" of
Number for
each of many ForkLengths?

If you simply use geom="line", things look clear and simple, no need
to bin
or simplify or...

If you do end up using geom="bar", I believe the mistake you're
making --
and I see an error message when I try -- is that you are using
scale_x_continuous whereas the X axis is discrete, so you should be using
scale_x_discrete. But then it will take some R magic to combine your
"bins"
into wider bins so you get a "less crowded" look.

Or perhaps I'm misunderstanding?

   Wayne


Felipe Carrillo wrote:
> 
> Hi: I need some help.
>  I am ploting a bar graph but I can't adjust my x axis scale
>  I use this code:
>       i <-  qplot(ForkLength,Number,data=FL,geom="bar")
>     i + geom_bar(colour="blue",fill="grey65") # too
crowded
> 
>      FL_dat <- ggplot(FL,aes(x=ForkLength,y=Number)) +
> geom_bar(colour="green",fill="grey65")
>     FL_dat + scale_x_continuous(limits=c(20,170)) # Can't see anything
>     
> FL	Number
> 29	22.9
> 30	63.4
> 31	199.3
> 32	629.6
> 33	2250.1
> ...
> 

-- 
View this message in context:
http://www.nabble.com/ggplot2-Xlim-tp21161660p21170453.html
Sent from the R help mailing list archive at Nabble.com.



------------------------------

Message: 15
Date: Thu, 25 Dec 2008 07:26:46 -0800 (PST)
From: 1Rnwb <sbpurohit@gmail.com>
Subject: Re: [R] creating standard curves for ELISA analysis
To: r-help@r-project.org
Message-ID: <21168216.post@talk.nabble.com>
Content-Type: text/plain; charset=us-ascii


Thank you for your suggestions,  I am sorry that 
http://www.nabble.com/file/p21168216/ds2_panelA_p8_B3_dil4x.csv
ds2_panelA_p8_B3_dil4x.csv I forgot to include the concentration of Standard
to use. the first standard (A1, A2) is 67000 and dilution series is created
by diluting it 1/3. i am reposting the full Absorbance data once again to
have a full idea about the output file created by the ELISA software. I am
also uploading the sample file for a better understanding,  serum samples in
this case are diluted 4 times

Location	 Sample	ENA-78(37)	FgF(54)	G-CSF(58)	GM-CSF(71)	IFNg(75)	IL-10(50)
IL-17(20)	IL-1b(6)	IL-1ra(16)	IL-2(17)	IL-4(21)	IL-5(09)	IL-6(32)	IL-8(36)
MCP1(78)	MIP-1a(59)	MIP-1b(74)	TNFa(77)	VEGF(52)
A1	s1	6923	12667.5	4644	3247	11878	11648	4142.5	6536.5	5409	7057.5	5146
10921	5437	7968	6590.5	11497.5	8358	8088.5	7721
B1	s2	4093	9680	2535.5	2000.5	8392	7017	2135	3913	3162	4226.5	2757	8132.5
2907	6416	6216	8332	5625.5	4364	4225
C1	s3	1689.5	4586	929	1020	4313	3549.5	961.5	2220.5	992	1585	1289.5	4309
1294	3748	4315	2780.5	2504	2043	1268
D1	s4	642	1440	352	418	1769	1327.5	318.5	824	409	528	399	1384	664	1693	1461
651	803	576.5	720
E1	s5	228.5	319.5	141	143	741	590.5	170	385	155	230	114	503.5	218	701	493
237	245	236	320
F1	s6	94	42	65	67	301	271	60	147	78	99.5	28	156.5	106	397	128	74	65	108	154
G1	s7	43	11	36	36	109	94	30.5	60.5	39	52	8	48	28	163	39	46	28	37	73.5
H1	b	15	2	12	15	7	4	3	4	19	6	2.5	0	0.5	78	23	12	16.5	3	30
A2	s1	6317	12543.5	4743	3757	10073.5	11016	4432	6990	5019	6687	4578	9856
5589.5	7265	6533	11368.5	7486	7503	7823
B2	s2	4487	9343.5	2114	2029	8300	6541	2027.5	4099	2986.5	3826	2857	7192.5
3197	5786	6386	7741	5086.5	4560.5	4409
C2	s3	1577	4024.5	942	1041	4035	3093.5	1098	1943	1133	1672	1263	3706	1421
3223	3729	2681	2065	1717	1453.5
D2	s4	609	1371.5	366.5	421.5	1884	1397	361	944	422	631	496	1442	535	1646
1523	766	791	718	723
E2	s5	234	304.5	143	165	760	541.5	160	358.5	163.5	249.5	111	459	222.5	765
416	188	215	235	281
F2	s6	90	44	64	68	268.5	218	55	135	73.5	102	25	140	101	304	104	72.5	57	87
120.5
G2	s7	39	9	34	35	90	90	31.5	57	38	47.5	9	42.5	25	133	38	42	29	33	70.5
H2	b	12	1.5	14	12	8	5	3	5	15	5	1.5	0	0.5	79.5	23.5	8	15	1	33
A3	1	683	5	23	23	16	10	16	9	66	10	6	4	12	653	641.5	23	22	14	182.5
B3	2	523	7	23	18	19.5	11	11	16	59.5	15.5	6	2	8.5	1789	369.5	26	28	16	140
C3	3	686.5	4	26	18	12	6	15.5	18.5	118	17	4	2	10	2040.5	714.5	20	28	17.5
123.5
D3	4	1640.5	5	71	17	17	9	13	16	564.5	13	5	4	18	1258	957	31	164	24.5	291
E3	5	158.5	5	34.5	20	15.5	8	13	13.5	75	13	4.5	4	50.5	1075	330	20.5	23	11	87
F3	6	862	5	21	22	18	10	14.5	12	58	18	4	2	7	2207	555.5	23	27	19	124
G3	7	710	6	30	16.5	19	6	13	14.5	105	17	6	2	12	1755.5	557	23	32	13	135
H3	8	1047	4	41	20	16	10.5	14	12	111	11	4.5	4	10.5	1690.5	404.5	23	50.5	23
212
A4	9	512.5	6.5	22	22	18	11	11	30	167	13	4	3	5	2729	1420	37	48	18	333
B4	10	979.5	5	20	27	19	2	18	15	122	14	4	2	7	1581	496	18	35	20	94
C4	11	270	6	23	20.5	19.5	9	14.5	68	656	15	3	4	79	5995	2964	50	40.5	17	198.5
D4	12	207	4	27	21	19	10	14	11	39	16	5	3	6.5	1622.5	311	25	25	15	181
E4	13	367	7.5	25	21	19	12	10	13	50	12	7	1	8.5	1395.5	718.5	24	30	19	219
F4	14	462	5	23	20	19	7.5	14	10	107	14	4	0	10	1715.5	484.5	23	22	19	265
G4	15	441.5	5	19	20	18	7	16	29	271	12	5	1	10	6917	6325	24.5	32	15.5	156
H4	16	521	7	38	22	18	10	14	11	164	16	4	3	23	1967	744	25	34.5	15	202
A5	17	759	5	18	21	16.5	10	10	10	55	11	4	1.5	16	1731.5	752	19	30	12	288
B5	18	624	6	22.5	20	21	12.5	11	12	52	12.5	4	2	8	2329.5	533	23	30.5	15	125
C5	19	735	5	21	19	14	10	17	11.5	291.5	9	5	3	10	773	1682.5	26	33	17.5	67
D5	20	450	5	25	16	15.5	8	17	12	65	12	5	3.5	9	1970	335	20	23	14	110
E5	21	405	5	21	18	14	7	12	10	139	13.5	5	0	8.5	1318	433	25.5	33.5	14	89
F5	22	155	3.5	12.5	10	12	4	35	6	24	4.5	3	2	8.5	391	257	19	114.5	8	104
G5	23	472	6	23	17	18	6	12	38.5	348	11	3	2	7	2764	1612	39	967	20	197
H5	24	326	5.5	20	19	17	7.5	13.5	11	44	13	5	3	66	1579	272.5	24	24	13	152.5
A6	25	341	5	24	22	15	8	13.5	13	68.5	12	4	2	7	1591	483	22	34	11	84
B6	26	460.5	5	21	23	16	10	11	160	677.5	11	5	2	23	5326	1495	46.5	62	19	138
C6	27	454	4	32.5	16.5	15	9	10.5	14	104.5	10.5	3	1	50	1468	1459	25.5	38	17
142
D6	28	604	6	27	18	16.5	7	14.5	37	950.5	12.5	5	4	14	5643	5980	24	36	18	324.5
E6	29	491	7	22.5	18	19	8	13.5	23	240	17	4	1	11	3802.5	1902	30.5	47.5	20	297
F6	30	414	4	24.5	20	20.5	9	13	14	39	16	3	3	6	1384.5	585.5	23	32	13	95
G6	31	423	5.5	21	19.5	19	9	16	299	1428	15	5	2	49	6343	6018	160.5	335	11.5
211
H6	32	286	6	28	18	10	9	14	13	108	13.5	3	2	27.5	1369	808	20	32	237	70
A7	33	874.5	6	23.5	20	16	8	12.5	65	588	6	5.5	3	17	5915	5098	36	81	23	229
B7	34	1211	3	23	20	16	33	12	9.5	78	9	4.5	2	8	2097	693.5	16.5	28	13	274.5
C7	35	257	4.5	25.5	17	16.5	9	12	10	52	10	5	3	7	1456	750	24	23	11	70.5


Thanks for the help

1Rnwb wrote:
> 
> Hello R guru's
> 
> I am a newbie to R, In my research work I usually generate a lot of ELISA
> data in form of absorbance values. I ususally use Excel to calculate the
> concentrations of unknown, but it is too tedious and manual especially
> when I have 100's of files to process. I would appreciate some help 
in
> creating a R script to do this with minimal manual input. s A1-G1 and
> A2-G2 are standards serially diluted H1 and H2 are Blanks. A3 to H12 are
> serum samples. I am pasting the structure of my data below:
> 
> 
> 
> A1		14821
> B1		11577
> C1		5781
> D1		2580
> E1		902
> F1		264
> G1		98
> H1		4
> A2		14569.5
> B2		11060
> C2		5612
> D2		2535
> E2		872
> F2		285
> G2		85
> H2		3
> A3		1016
> B3		2951.5
> C3		547
> D3		1145
> E3		4393
> F3		4694
> G3		1126
> H3		1278
> A4		974.5
> B4		3112.5
> C4		696.5
> D4		2664.5
> E4		184.5
> F4		1908
> G4		108.5
> H4		1511
> A5		463.5
> B5		1365
> C5		816
> D5		806
> E5		1341
> F5		1157
> G5		542.5
> H5		749
> 
> 

-- 
View this message in context:
http://www.nabble.com/creating-standard-curves-for-ELISA-analysis-tp20917182p21168216.html
Sent from the R help mailing list archive at Nabble.com.



------------------------------

Message: 16
Date: Fri, 26 Dec 2008 00:29:17 +0000 (UTC)
From: Ben Bolker <bolker@ufl.edu>
Subject: Re: [R] Percent damage distribution
To: r-help@stat.math.ethz.ch
Message-ID: <loom.20081226T002735-750@post.gmane.org>
Content-Type: text/plain; charset=us-ascii

diegol <diegol81 <at> gmail.com> writes:

> 
> 
> R version: 2.7.0
> Running on: WinXP
> 
> I am trying to model damage from fire losses (given that the loss
occurred).
> Since I have the individual insured amounts, rather than sampling dollar
> damage from a continuous distribution ranging from 0 to infinity, I want
to
> sample from a percent damage distribution from 0-100%. One obvious
solution
> is to use runif(n, min=0, max=1), but this does not seem to be a good
idea,
> since I would not expect damage to be uniform.
> 


 Beta distribution (rbeta(...)) or
logistic-binomial distribution
plogis(rnorm(...)) .

  See e.g. 

Smithson, Michael, and Jay Verkuilen. 2006. A better lemon squeezer?
Maximum-likelihood regression with beta-distributed dependent variables.
Psychological Methods 11, no. 1 (March): 54-71. doi:2006-03820-004.



------------------------------

Message: 17
Date: Fri, 26 Dec 2008 08:44:19 +0000 (GMT)
From: Prof Brian Ripley <ripley@stats.ox.ac.uk>
Subject: Re: [R] How can I avoid nested 'for' loops or quicken the
	process?
To: Oliver Bandel <oliver@first.in-berlin.de>
Cc: r-help@stat.math.ethz.ch
Message-ID: <alpine.LFD.2.00.0812260833330.4072@gannet.stats.ox.ac.uk>
Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed

On Thu, 25 Dec 2008, Oliver Bandel wrote:

> Bert Gunter <gunter.berton <at> gene.com> writes:
>
>>
>> FWIW:
>>
>> Good advice below! -- after all, the first rule of optimizing code is:
>> Don't!
>>
>> For the record (yet again), the apply() family of functions (and their
>> packaged derivatives, of course) are "merely" vary carefully
written for()
>> loops: their main advantage is in code readability, not in efficiency
gains,
>> which may well be small or nonexistent. True efficiency gains require
>> "vectorization", which essentially moves the for() loops
from interpreted
>> code to (underlying) C code (on the underlying data structures): e.g.
>> compare rowMeans() [vectorized] with ave() or apply(..,1,mean).
> [...]
>
> The apply-functions do bring speed-advantages.
>
> This is not only what I read about it,
> I have used the apply-functions and really got
> results faster.
>
> The reason is simple: an apply-function does
> make in C, what otherwise would be done on the level of R
> with for-loops.

Not true of apply(): true of lapply() and hence sapply().  I'll leave you 
to check eapply, mapply, rapply, tapply.

So the issue is what is meant by 'the apply() family of functions':
people 
often mean *apply(), of which apply() is an unusual member, if one at all.

[Historical note: a decade ago lapply was internally a for() loop.  I 
rewrote it in C in 2000: I also moved apply to C at the same time but it 
proved too little an advantage and was reverted.  The speed of lapply 
comes mainly from reduced memory allocation: for() is also written in C.]

-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595



------------------------------

Message: 18
Date: Fri, 26 Dec 2008 08:55:33 +0000 (GMT)
From: Prof Brian Ripley <ripley@stats.ox.ac.uk>
Subject: Re: [R] Percent damage distribution
To: diegol <diegol81@gmail.com>
Cc: r-help@r-project.org
Message-ID: <alpine.LFD.2.00.0812260849000.4072@gannet.stats.ox.ac.uk>
Content-Type: text/plain; charset="utf-8"; Format="flowed"

Not an R question as yet .....

In my limited experience (we have some insurance projets), 100% can occur, 
but otherwise a beta distbribution may suit, which suggests a mixture 
distribution.  But start with an empirical examination (histogram, ecdf, 
density plot) of the distribution, since it may reveal other features.

The next question is 'why model'?   For such a simple problem (a 
univariate distribution) a plot may be a sufficent analysis, and for e.g. 
simulation you could just re-sample the data.

On Thu, 25 Dec 2008, diegol wrote:

>
> R version: 2.7.0
> Running on: WinXP
>
> I am trying to model damage from fire losses (given that the loss
occurred).
> Since I have the individual insured amounts, rather than sampling dollar
> damage from a continuous distribution ranging from 0 to infinity, I want
to
> sample from a percent damage distribution from 0-100%. One obvious
solution
> is to use runif(n, min=0, max=1), but this does not seem to be a good
idea,
> since I would not expect damage to be uniform.
>
> I have not seen such a distribution in actuarial applications, and rather
> than inventing one from scratch I thought I'd ask you if you know one,
maybe
> from other disciplines, readily available in R.
>
> Thank you in advance.
>
> -----
> ~~~~~~~~~~~~~~~~~~~~~~~~~~
> Diego Mazzeo
> Actuarial Science Student
> Facultad de Ciencias Econ?micas
> Universidad de Buenos Aires
> Buenos Aires, Argentina
> -- 
> View this message in context:
http://www.nabble.com/Percent-damage-distribution-tp21170344p21170344.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> 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.
>

-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

------------------------------

Message: 19
Date: Fri, 26 Dec 2008 1:08:40 -0800
From: <rkevinburton@charter.net>
Subject: [R] Upgrading R causes Tinn-R to freeze.
To: r-help@stat.math.ethz.ch
Message-ID: <20081226040840.0CUYA.1997811.root@mp18>
Content-Type: text/plain; charset=utf-8

I recently upgraded from 2.8.0 to 2.8.1 by first installing the 2.8.1 version
then copying the binaries and the library to the 2.8.0 folder. Now Tinn-R will
not start up. I just see that start up splash screen for a long period of time.
It seems fozen to me. Any guesses on what I did wrong in the upgrade?

Thank you.

Kevin



------------------------------

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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

End of R-help Digest, Vol 70, Issue 26
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