Nabble - R

Re: Incomplete, unbalanced design, and pseudoreplication?

2009-11-09T05:23:19Z

Jennifer Mollon wrote:

The response is a measurement - the amount of DNA extracted during the
experiment. There were 2 factors to be tested - one is the condition
under which the experiment took place and the other is the type of DNA
to be extracted. Each set of factors was replicated, so condition A
and DNA type A were tested twice using the same input material.
Finally, the whole experiment was repeated twice, but in one of the
experiments there was not enough input material and one of the DNA
types (call it type D) was not tested at all, but all other levels of
that factor and the condition factor were tested. From this, I think:
....

This is a classical case for procedure lme in package nlme which is quite robust for unbalanced designs. Arrange your data in the long form; preferably use characters instead of numbers to describe the levels.
Check the book by Pinheiro/Bates for details; or, as a starter, the examples in library/nlme/scripts.

Dieter

Condition DNA Run Amount
CondA DNAA Run1 33
CondA DNAB Run1 22

From forum: R help

Re: \\signature macro generated by promptMethods

2009-11-09T05:12:12Z

On 09/11/2009 7:39 AM, Romain Francois wrote:
> Hello,
>
> promptMethods generate a macro for signatures, but the macro does not
> exist in the Rd parser.

Right, that's not supposed to be a macro. See ?cbind2 for an example,

\item{\code{signature(x = "ANY", y = "ANY")}}{the default method
using \R's internal code.}

Duncan Murdoch

>
> > setClass("track",
> + representation(x="numeric", y="numeric"))
> [1] "track"
> > setGeneric("foo", function(x){ standardGeneric("foo") } )
> [1] "foo"
> > setMethod( "foo", "track", function(x){ NULL } )
> [1] "foo"
> > promptMethods( "foo" )
> A shell of methods documentation has been written to the file
> ‘foo-methods.Rd’.
> > writeLines( readLines( "foo-methods.Rd" ) )
> \name{foo-methods}
> \docType{methods}
> \alias{foo-methods}
> \alias{foo,track-method}
> \title{ ~~ Methods for Function foo ~~}
> \description{
> ~~ Methods for function \code{foo} ~~
> }
> \section{Methods}{
> \describe{
> \item{\code{\signature(x = "track")}}{ ~~describe this method here }
> }}
> \keyword{methods}
> \keyword{ ~~ other possible keyword(s)}
>
> Romain
>

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From forum: R devel

Re: Quickly generate all possible combinations of 2 groups

2009-11-09T05:08:57Z

In case anyone is interested in the random variant, here's some code:

http://gist.github.com/229929

--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

Looking to arrange a meeting? Check my public calendar:
http://tr.im/mikes_public_calendar

~ Certainty is folly... I think. ~

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From forum: R help

Re: How to change color the default in levelplot() ?

2009-11-09T04:55:19Z

Here is an example of how I do it using the 'col.regions' parameter:

# create color palette
col.l <- colorRampPalette(c('blue', 'green', 'purple', 'yellow', 'red'))(30)

levelplot(aisle ~ store * pog, storePOG, col.regions=col.l,
cuts=diff(range(as.numeric(as.character(storePOG$aisle)), na.rm=TRUE)),
scales=list(x=list(rot=90, cex=0.5)),
panel=function(x, y, subscripts, ...){
panel.levelplot(x, y, subscripts, ...)
ltext(x,y,storePOG$lr[subscripts], fontface="bold", cex=.6)
},
xlab="Store", ylab="Planogram", main="Physical Aisle Locations")

On Sun, Nov 8, 2009 at 10:15 PM, BabyL BabyK <babyl.babyk@...> wrote:

> Dear R communities
>
> May I seek your advices on how to change color the default in levelplot(), e.g. from the default of pink and light blue, to e.g. red and green ?
> The levelplot function has 1 of the arguments being panel (which is actually panel.levelplot), but I am not sure where the commands to alter the color.
>
> For example, I type:
> p1<-levelplot(my.mat,colorkey=FALSE),
> how could I include col.regions of panel.levelplot?
> And whether it is right to use the col.regions?
>
> I am using R 2.8.1 in ubuntu.
>
> Many thanks and have a good day!
>
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@... 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.
>

--
Jim Holtman
Cincinnati, OH
+1 513 646 9390

What is the problem that you are trying to solve?

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From forum: R help

pvclust with squared euclidean distance

2009-11-09T04:52:51Z

hi,

I'm trying to use pvclust function (pvclust package) to perform a
cluster analysis and finding p-values for groups, however, i need to
use the "squared euclidean distance" method instead of "euclidean
distance" as distance matrix but it seems that the package only allows
the last... any ideas how can I use squared distances instead of the
simple ones in this function?

Many thanks!

--
Marcos Antonio Carvajalino Fernández
Estudiante de Ingeniería Ambiental y Sanitaria
Universidad del Magdalena
Santa Marta, Colombia

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From forum: R help

Quickly generate all possible combinations of 2 groups

2009-11-09T04:42:51Z

Hi all,

I suspect the answer to this query will be the tongue-in-cheek "use a
quantum computer", but I thought my understanding might be
sufficiently limited that I'm missing a simpler option.

I'm looking for a way to cycle through all possible combinations of 2
groups of data. For 10 data points, that's 2^10 combinations, for 20
data points it's 2^20, etc. combn() from the combinat package is
useful to this purpose, as in:

########
library(combinat)
n=20 #number of data points in the data set
start=proc.time()[3] #start a timer
pb = txtProgressBar(max=n+1,style=3) #initialize a progress bar
for(i in 0:n){ #for each possible size of set1
Set1 = combn(1:n,i) #get set1 combinations
Set2 = rev(combn(1:n,n-i)) #get set2 combinations
setTxtProgressBar(pb,i+1) #increment the progress bar
}
close(pb) #close the progress bar
proc.time()[3]-start #show the time taken (about 40s @ 2.2GHz)
########

However, this obviously ends up being too slow when the number of data
points rises much above 20 (I'll likely be dealing with data sets to a
maximum of 200 points).

In case it's relevant, the motivation behind this problem is that I'm
seeking an alternative to EM or simplex methods to obtaining the MLE
of mixture data. Given a mixture model consisting of 2 distributions,
I should be able to obtain an MLE by finding the partitioning of the
data into 2 groups that yields the highest likelihood. I'm
specifically looking at modelling circular data by a mixture of
uniform and a Von Mises centered on zero, so once I have a given
partition, I can estimate parameters of the model (proportion of
points drawn from the Von Mises and concentration of the Von Mises)
analytically and compute the likelihood of the data given that pair of
parameters. I've coded a variant of this approach that generates
random partitioning of data, and this seems to to a decent job of
generating something that might be useful as a starting point for a
subsequent EM or simplex search, but I thought I might double check
with the list to see if there's a computationally efficient solution
to the "test all combinations" scheme.

Cheers,

Mike

--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

Looking to arrange a meeting? Check my public calendar:
http://tr.im/mikes_public_calendar

~ Certainty is folly... I think. ~

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From forum: R help

\\signature macro generated by promptMethods

2009-11-09T04:39:39Z

Hello,

promptMethods generate a macro for signatures, but the macro does not
exist in the Rd parser.

> setClass("track",
+ representation(x="numeric", y="numeric"))
[1] "track"
> setGeneric("foo", function(x){ standardGeneric("foo") } )
[1] "foo"
> setMethod( "foo", "track", function(x){ NULL } )
[1] "foo"
> promptMethods( "foo" )
A shell of methods documentation has been written to the file
‘foo-methods.Rd’.
> writeLines( readLines( "foo-methods.Rd" ) )
\name{foo-methods}
\docType{methods}
\alias{foo-methods}
\alias{foo,track-method}
\title{ ~~ Methods for Function foo ~~}
\description{
~~ Methods for function \code{foo} ~~
}
\section{Methods}{
\describe{
\item{\code{\signature(x = "track")}}{ ~~describe this method here }
}}
\keyword{methods}
\keyword{ ~~ other possible keyword(s)}

Romain

--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
http://romainfrancois.blog.free.fr
|- http://tr.im/EAD5 : LondonR slides
|- http://tr.im/BcPw : celebrating R commit #50000
`- http://tr.im/ztCu : RGG #158:161: examples of package IDPmisc

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From forum: R devel

Re: Bug in all.equal() or in the plm package

2009-11-09T03:24:05Z

Arne Henningsen wrote:
> Hi!
>
> I noticed that there is a (minor) bug either the command all.equal()
> or in the "plm" package. I demonstrate this using an example taken
> from the documentation of plm():
>

I'm not sure this is a bug, but I'd call it at least a design flaw. The
problem is that the length.Formula method in the Formula package (which
plm depends on) returns a vector of length 2. Now there's nothing in R
that requires length() to return a scalar, but all.equal assumes it
does, and I'd guess there are lots of other places this assumption is made.

Duncan Murdoch

> ======================================
> R> data("Produc", package="plm")
> R> zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,
> + data=Produc, index=c("state","year"))
> R> all.equal(zz,zz)
> [1] TRUE
> Warning message:
> In if (length(target) != length(current)) return(paste("target,
> current differ in having response: ", :
> the condition has length > 1 and only the first element will be used
>
>> all.equal(zz$formula,zz$formula)
>>
> [1] TRUE
> Warning message:
> In if (length(target) != length(current)) return(paste("target,
> current differ in having response: ", :
> the condition has length > 1 and only the first element will be used
>
>> class(zz$formula)
>>
> [1] "pFormula" "Formula" "formula"
> ======================================
>
> The last commands show that the warning message comes from comparing
> the elements "formula", which are of the class "pFormula" (inheriting
> from "Formula" and "formula"). It would be great if this issue could
> be fixed in the future.
>
> Thanks a lot,
> Arne
>
>

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From forum: R devel

Bug in all.equal() or in the plm package

2009-11-09T02:29:54Z

Hi!

I noticed that there is a (minor) bug either the command all.equal()
or in the "plm" package. I demonstrate this using an example taken
from the documentation of plm():

======================================
R> data("Produc", package="plm")
R> zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,
+ data=Produc, index=c("state","year"))
R> all.equal(zz,zz)
[1] TRUE
Warning message:
In if (length(target) != length(current)) return(paste("target,
current differ in having response: ", :
the condition has length > 1 and only the first element will be used
> all.equal(zz$formula,zz$formula)
[1] TRUE
Warning message:
In if (length(target) != length(current)) return(paste("target,
current differ in having response: ", :
the condition has length > 1 and only the first element will be used
> class(zz$formula)
[1] "pFormula" "Formula" "formula"
======================================

The last commands show that the warning message comes from comparing
the elements "formula", which are of the class "pFormula" (inheriting
from "Formula" and "formula"). It would be great if this issue could
be fixed in the future.

Thanks a lot,
Arne

--
Arne Henningsen
http://www.arne-henningsen.name

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From forum: R devel

Re: negative log likelihood

2009-11-09T02:23:07Z

Sounds like a homework question ...

if y = a + bx + e, where e ~ N(0, sigma^2)

the log likelihood of the slope parameter and intercept parameters, a and b, and variance sigma^2 given n data points y and covariates x is

f(a,b, sigma; y, x) = -0.5*n*log(2 * pi) - n*log(sigma) - 0.5 / sigma^2 * sum_i [ (y_i - (a + b*x_i))^2 ]

You can simplyfiy this function if you condition on the variance and intercept.
You are then left with a simple function coresponding to the log-likelihood curve.
This function is trivial to minimise.

Hope this helps.
Colin.

________________________________

From: r-help-bounces@... on behalf of mat7770
Sent: Sun 08/11/2009 19:12
To: r-help@...
Subject: [R] negative log likelihood

I have two related variables, each with 16 points (x and Y). I am given
variance and the y-intercept. I know how to create a regression line and
find the residuals, but here is my problem. I have to make a loop that uses
the seq() function, so that it changes the slope value of the y=mx + B
equation ranging from 0-5 in increments of 0.01. The loop also needs to
calculate the negative log likelihood at each slope value and determine the
lowest one. I know that R can compute the best regression line by using
lm(y~x), but I need to see if that value matches the loop functions.
--
View this message in context: http://old.nabble.com/negative-log-likelihood-tp26256881p26256881.html
Sent from the R help mailing list archive at Nabble.com.

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From forum: R help

Re: Hand-crafting an .RData file

2009-11-09T02:19:12Z

You can try read.csv.sql in the sqldf package. It reads a file into an
sqlite database which it creates for you using RSQLite/sqlite thus
effectively its done outside of R. Then it extracts the portion you
specify using an sql statement and destroys the database. Omit the
sql statement if you want the entire file. Don't know if its faster
than read.table when used in that way but its only one line of code so
you could easily try it. See example 13 on home page:
http://sqldf.googlecode.com

On Mon, Nov 9, 2009 at 12:27 AM, Adam D. I. Kramer <adik@...> wrote:

> Hello,
>
> I frequently have to export a large quantity of data from some
> source (for example, a database, or a hand-written perl script) and then
> read it into R. This occasionally takes a lot of time; I'm usually using
> read.table("filename",comment.char="",quote="") to read the data once it is
> written to disk.
>
> However, I *know* that the program that generates the data is more
> or less just calling printf in a for loop to create the csv or tab-delimited
> file, writing, then having R parse it, which is pretty inefficient. Instead,
> I am interested in figuring out how to write the data in .RData
> format so that I can load() it instead of read.table() it.
>
> Trolling the internet, however, has not suggested anything about the
> specification for an .RData file. Could somebody link me to a specification
> or some information that would instruct me on how to construct a .RData
> file (either compressed or uncompressed)?
>
> Also, I am open to other suggestions of how to get load()-like
> efficiency in some other way.
>
> Many thanks,
> Adam D. I. Kramer
>
> ______________________________________________
> R-help@... 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.
>

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and provide commented, minimal, self-contained, reproducible code.

From forum: R help

Incomplete, unbalanced design, and pseudoreplication?

2009-11-09T01:57:45Z

Hello,

I am trying to help someone who has carried out an experiment and I'm
finding it quite difficult to understand the appropriate model to use
& code it.

The response is a measurement - the amount of DNA extracted during the
experiment. There were 2 factors to be tested - one is the condition
under which the experiment took place and the other is the type of DNA
to be extracted. Each set of factors was replicated, so condition A
and DNA type A were tested twice using the same input material.
Finally, the whole experiment was repeated twice, but in one of the
experiments there was not enough input material and one of the DNA
types (call it type D) was not tested at all, but all other levels of
that factor and the condition factor were tested. From this, I think:

1. The replicates within each experiment are pseudoreplicates - there
are pairs of measures with the same input material, and both factor
levels are the same.
2. The 2 experiments can be treated as blocks, but they are not
balanced or complete.

There are 2 questions of interest to the experimenter:

1. Does the amount of DNA extracted differ for the different DNA types
under the different conditions?
2. One of the conditions is new, and of particular interest. Under
this condition, are there significantly different amounts of DNA
extracted depending on DNA type? There are 2 particular contrasts of
interest here, call them DNA types B&C vs A, and B&C vs D. DNA type D
is only tested in the second experiment.

I would be very grateful for comments about the analysis of this
complicated data set. Are my beliefs above correct, regarding the
design? If so, which R packages and methods can help me with this
analysis? In particular, how should the error term be structured for
this design? And finally, are the 2 research questions best answered
by 2 separate analyses (e.g. the second one looking at only the one
condition in isolation), or can a single analysis of a full model
answer both of these questions?

Many thanks for your consideration and time,
Jen

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From forum: R help

ImproveProb Function in Design Package

2009-11-09T01:44:57Z

Hello

Thanks to Frank Harrell for the great Design package. I noted from Pencina's Article (Statistics in Medicine Vol 27, pp157-172) that the result obtained for the Net Reclassification Index depends upon categorical cut-offs for risk (eg in the paper <6%, 6-20% and >20%). I wondered how the improveprob function arrives at its cut-offs, and whether there was any function to change the default cut-offs used.

Thanks for any help you can offer.

Yours sincerely

David McAllister

*****************************************************************
The information contained in this message may be confide...{{dropped:8}}

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From forum: R help

Re: Antw: Re: compiling R-2.9.2 or R-2.10.0 on ubuntu 9.04 (powerpc)

2009-11-09T01:27:50Z

Laurin Müller wrote:

> i installed:
> libreadline-dev
> libcnf-dev
>
> but configure with no readline brings the same error.
>
> regards,
> laurin
>
> >>> Paul Hiemstra <p.hiemstra@...> 05.11.2009 12:38 >>>
> sudo apt-get install libreadline-dev

Try running with readline turned on (the default). In regard to your
problem, check the value of the LDFLAGS and have a look at the following
e-mail from the archive (found by googling your error message):

http://www.mail-archive.com/r-help@.../msg20344.html

cheers,
Paul

--
Drs. Paul Hiemstra
Department of Physical Geography
Faculty of Geosciences
University of Utrecht
Heidelberglaan 2
P.O. Box 80.115
3508 TC Utrecht
Phone: +3130 274 3113 Mon-Tue
Phone: +3130 253 5773 Wed-Fri
http://intamap.geo.uu.nl/~paul

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From forum: R help

Re: order of points in spline

2009-11-09T01:14:13Z

At the end, although the xspline function suggested by Greg did what I needed, it suffers from the fact that you cannot specify the number of points of the output curve.
I then wrote my function for a generic nth grade Bezier curve interpolation.
You can find the code here if ever you're interested.
http://rosettacode.org/wiki/Cubic_bezier_curves#R

>The spline function (and loess and others) fit regression splines (or approximations) meaning that each value of
>x maps to only 1 value of y. If you want a curve that goes through points in the specified order and is able to
>wrap back then you probably want the xspline function. See its help page for details.
>
>Hope this helps,
>
>--
>Gregory (Greg) L. Snow Ph.D.
>Statistical Data Center
>Intermountain Healthcare
>greg.snow@imail.org
>801.408.8111

> -----Original Message-----
> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Nicola Romanò
> Sent: Friday, November 06, 2009 2:07 PM
> To: r-help@r-project.org
> Subject: Re: [R] order of points in spline
>
> > Huh?
> >
> > Prevent spline from using (x[n],y[n]) as the coordinates of the
> n^{th} data point?
> >
> > Why on earth would you want to do that?
> >
>
> Maybe I did not explain myself correctly.
>
> My first three points have x: 262, 275, 264
>
> I want the spline to go from point 1 (x=262) to point 2 (x=275) and
> then go back on the x axis to point 3 (x=264).
> The function goes from 1 (x=262) to 3 (x=264) to 2 (x=275)
>
> Please, run my example and you should see for yourself.
>
> ______________________________________________
> 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.

______________________________________________
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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.

From forum: R help

Re: negative log likelihood

2009-11-09T00:49:13Z

On Sun, 2009-11-08 at 11:12 -0800, mat7770 wrote:
> I have two related variables, each with 16 points (x and Y). I am given
> variance and the y-intercept. I know how to create a regression line and
> find the residuals, but here is my problem. I have to make a loop that uses
> the seq() function, so that it changes the slope value of the y=mx + B
> equation ranging from 0-5 in increments of 0.01. The loop also needs to
> calculate the negative log likelihood at each slope value and determine the
> lowest one. I know that R can compute the best regression line by using
> lm(y~x), but I need to see if that value matches the loop functions.

Hi

If I understand your question you need extract log-likelihood for a
linear model, so you need using logLik command, see example:

set.seed(1)
x<-rpois(16,6)
y<-2*x+3+rnorm(16,sd=3)
model<-lm(y~x)
logLik(model)
'log Lik.' -40.1177 (df=3)

--
Bernardo Rangel Tura, M.D,MPH,Ph.D
National Institute of Cardiology
Brazil

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From forum: R help

How to identify what is used as EOL in a given file?

2009-11-09T00:21:50Z

Hi,

Is there any R-generic, OS-agnostic way to figure out what end-of-line
character is being used in a file to be processed by "readLines"?

Thanks, Joh

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From forum: R help

Re: Hand-crafting an .RData file

2009-11-09T00:05:38Z

The R 'save' format (as used for the saved workspace .RData) is
described in the 'R Internals' manual (section 1.8). It is intended
for R objects, and you would first have to create one[*] of those in
your other application. That seems a lot of work.

The normal way to transfer numeric data between applications is to
write a binary file: R can read such files with readBin(), and it also
has wrappers/C-code to read a number of commmon binary data formats
(e.g. those from SPSS).

With character data there are more issues (and more formats, see also
readChar()), but load() is not particularly fast for those.

Ultimately the R functions pay a performance price for their
flexibility so hand-crafted C code to read the format can be
worthwhile: but see the comments below about whether I/O speed is
that important.

[*] the 'save' format is a serialization of a single R object, even if
you save many objects, since the object(s) are combined into a
pairlist.

On Sun, 8 Nov 2009, Adam D. I. Kramer wrote:

> Hello,
>
> I frequently have to export a large quantity of data from some
> source (for example, a database, or a hand-written perl script) and then
> read it into R. This occasionally takes a lot of time; I'm usually using
> read.table("filename",comment.char="",quote="") to read the data once it is
> written to disk.

Specifying colClasses and nrows will usually help.

To read from a database, packages such as RODBC use binary data
transfer: with suitable tuning this can be fast.

> However, I *know* that the program that generates the data is more
> or less just calling printf in a for loop to create the csv or tab-delimited
> file, writing, then having R parse it, which is pretty inefficient. Instead,
> I am interested in figuring out how to write the data in .RData
> format so that I can load() it instead of read.table() it.

Without more details it is hard to say if it is inefficient.
read.table() can read data pretty fast (millions of items per second)
if used following the hints in the 'R Data' manual. See e.g.
https://stat.ethz.ch/pipermail/r-devel/2004-December/031733.html

Almost anything non-trivial one might do with such data is much
slower. The trend is to write richer (and slower to read) data
formats.

> Trolling the internet, however, has not suggested anything about the
> specification for an .RData file. Could somebody link me to a specification
> or some information that would instruct me on how to construct a .RData
> file (either compressed or uncompressed)?
>
> Also, I am open to other suggestions of how to get load()-like
> efficiency in some other way.
>
> Many thanks,
> Adam D. I. Kramer

--
Brian D. Ripley, ripley@...
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

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From forum: R help

Re: Models for Discrete Choice in R

2009-11-08T22:22:15Z

Le dimanche 08 novembre 2009 à 19:05 -0600, Frank E Harrell Jr a écrit :

> Emmanuel Charpentier wrote:
> > Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
> >> Hi,
> >>
> >> I would like to fit Logit models for ordered data, such as those
> >> suggested by Greene (2003), p. 736.
> >>
> >> Does anyone suggests any package in R for that?
> >
> > look up the polr function in package MASS (and read the relevant pages
> > in V&R4 and some quoted references...) or the slightly more
> > sophisticated (larger range of models) lrm function in F. Harrell's
> > Design (now rms) packge (but be aware that Design is a huge beast witch
> > carries its own "computing universe", based on (strong) Harrell's view
> > of what a regression analysis should be : reading his book is, IMHO,
> > necessary to understand his choices and agree (or disgree) with them).
> >
> > If you have a multilevel model (a. k. a. one "random effect" grouping),
> > the "repolr" packge aims at that, but I've been unable to use it
> > recently (numerical exceptions).
> >
> >> By the way, my dependent variable is ordinal and my independent
> >> variables are ratio/intervalar.
> >
> > Numeric ? Then maybe some recoding/transformation is in order ... in
> > which case Design/rms might or might not be useful.
>
> I'm not clear on what recoding or transformation is needed for an
> ordinal dependent variable and ratio/interval independent variables, nor
> why rms/Design would not be useful.

I was thinking about transformations/recoding of the *independent*
variables...

Emmanuel Charpentier

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From forum: R help

Hand-crafting an .RData file

2009-11-08T21:27:34Z

Hello,

I frequently have to export a large quantity of data from some
source (for example, a database, or a hand-written perl script) and then
read it into R. This occasionally takes a lot of time; I'm usually using
read.table("filename",comment.char="",quote="") to read the data once it is
written to disk.

However, I *know* that the program that generates the data is more
or less just calling printf in a for loop to create the csv or tab-delimited
file, writing, then having R parse it, which is pretty inefficient.
Instead, I am interested in figuring out how to write the data in .RData
format so that I can load() it instead of read.table() it.

Trolling the internet, however, has not suggested anything about the
specification for an .RData file. Could somebody link me to a specification
or some information that would instruct me on how to construct a .RData
file (either compressed or uncompressed)?

Also, I am open to other suggestions of how to get load()-like
efficiency in some other way.

Many thanks,
Adam D. I. Kramer

______________________________________________
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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.

From forum: R help

Bug in gplots::heatmap.2 symbreaks arg default (?)

2009-11-08T19:22:12Z

Hi all,

I think the code to calculate the default value of the symbreaks
argument in the gplots::heatmap.2 function is wrong.

The documentation says symbreaks defaults to true if any negative
values are detected in the data passed in.

The relevant code in the parameter list of this function definition
(gplots 2.7.3) is this:

symbreaks = min(x < 0, na.rm = TRUE) || scale != "none"

When I'm pretty sure it should be:

symbreaks = min(x, na.rm = TRUE) < 0 || scale != "none"

Likewise for the symkey parameter.

Right?

Thanks,
-steve

--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact

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From forum: R help

How to change color the default in levelplot() ?

2009-11-08T19:15:04Z

Dear R communities

May I seek your advices on how to change color the default in levelplot(), e.g. from the default of pink and light blue, to e.g. red and green ?
The levelplot function has 1 of the arguments being panel (which is actually panel.levelplot), but I am not sure where the commands to alter the color.

For example, I type:
p1<-levelplot(my.mat,colorkey=FALSE),
how could I include col.regions of panel.levelplot?
And whether it is right to use the col.regions?

I am using R 2.8.1 in ubuntu.

Many thanks and have a good day!

[[alternative HTML version deleted]]

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From forum: R help

Forward selection peculiarity

2009-11-08T19:08:21Z

Hi all,

I'm using the mle.stepwise function in the wle package to run forward selection. My statistical background tells me that the first variable selected should be the one with the highest correlation with the response, however that's not the case. The two highest correlations with the response are "similar" (0.86 and 0.89) however the forward selection selects the variable with 0.86 correlation first and then in the next step chooses the variable with correlation 0.89.

Any thoughts on this? Is is this some type of bug in the function or package?

Thanks

From forum: R help

RcppTemplate

2009-11-08T17:48:09Z

The Rcpp R/C++ object mapping library and package template has
been updated on CRAN in package RcppTemplate. It allows you
to work with R objects like data frames and zoo time series in
C++ programs. R can call C++ functions, and C++ objects can
call R functions, with parameters and return values of any
of the most commonly used R data types (data frames,
factors, time series, etc.). By using as.zoo, as.irts, as.xts, etc. it
is possible to work with the other commonly used time series
on the C++ side. The main design goal was improved performance.

Dominick

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From forum: R devel

RcppTemplate

2009-11-08T17:36:25Z

From forum: R devel

RcppTemplate

2009-11-08T17:36:25Z

The Rcpp R/C++ object mapping library and package template has
been updated on CRAN in package RcppTemplate. It allows you
to work with R objects like data frames and zoo time series in
C++ programs. R can call C++ functions, and C++ objects can
call R functions, with parameters and return values of any
of the most commonly used R data types (data frames,
factors, time series, etc.). By using as.zoo, as.irts, as.xts, etc. it
is possible to work with the other commonly used time series
on the C++ side. The main design goal was improved performance.

Dominick

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-- Subscriber-posting only.
-- If you want to post, subscribe first.

From forum: Rmetrics

Re: reference on contr.helmert and typo on its help page.

2009-11-08T17:32:41Z

Dear Peng,

I'm tempted to try to get an entry in the fortunes package but will instead
try to answer your questions directly:

> -----Original Message-----
> From: r-help-bounces@... [mailto:r-help-bounces@...]
On

> Behalf Of Peng Yu
> Sent: November-08-09 7:41 PM
> To: r-help@...
> Subject: Re: [R] reference on contr.helmert and typo on its help page.
>
> Dear John,
>
> I did read Section 9.1.2 and various other textbooks before posting my
> questions. But each reference uses slightly different notations and
> terminology. I get confused and would like a description that
> summaries everything so that I don't have to refer to many different
> resources. May I ask a few questions on the section in your textbook?
>
> Which variable in Section 9.1.2 is "a matrix of contrasts" mentioned
> in the help page of 'contr.helmert'? Which matrix of contrast in R
> corresponds to dummy regression? With different R formula, e.g. y ~ x
> vs. y ~ x -1, $X_F$ (mentioned on page 189) is different and hence
> $\beta_F$ (mentioned in eq. 9.3) is be different. So my understanding
> is that the matrix of contrast should depend on the formula. But it is
> not according to the help page of "contr.helmert".

If the model is simply y ~ A, for the factor A, then cbind(1, contrasts(A))
is what I call X_B, the row-basis of the model matrix. As I explain in the
section that you read, the level means are mu = X_B beta, and thus beta =
X_B^-1 mu = 0 are the hypotheses tested by the contrasts. Moreover, if, as
in Helmert contrasts, the columns of X_B are orthogonal, then so are the
rows of X_B^-1, and the latter are simply rescalings of the former. That
allows one conveniently to code the hypotheses directly in X_B; all this is
also explained in that section of my book, and is essentially what Peter D.
told you. In R, contr.treatment and contr.SAS provide dummy-variable (0/1)
coding of regressors, differing only in the selection of the reference
level.

John

>
> Regards,
> Peng
>
> On Sun, Nov 8, 2009 at 6:17 PM, John Fox <jfox@...> wrote:
> > Dear Peng Yu,
> >
> > Perhaps you're referring to my text, Applied Linear Regression Analysis
and
> > Generalized Linear Models, since I seem to recall that you sent me a
number
> > of questions about it. See Section 9.1.2 on linear contrasts for the
answer

> > to your question.
> >
> > I hope this helps,
> > John
> >
> > --------------------------------
> > John Fox
> > Senator William McMaster
> > Professor of Social Statistics
> > Department of Sociology
> > McMaster University
> > Hamilton, Ontario, Canada
> > web: socserv.mcmaster.ca/jfox
> >
> >
> >> -----Original Message-----
> >> From: r-help-bounces@...

[mailto:r-help-bounces@...]

> > On
> >> Behalf Of Peng Yu
> >> Sent: November-08-09 4:52 PM
> >> To: r-help@...
> >> Subject: Re: [R] reference on contr.helmert and typo on its help page.
> >>
> >> On Sun, Nov 8, 2009 at 11:28 AM, Peter Dalgaard
> >> <p.dalgaard@...> wrote:
> >> > Gabor Grothendieck wrote:
> >> >>
> >> >> On Sun, Nov 8, 2009 at 11:59 AM, Peng Yu <pengyu.ut@...>

wrote:
> >> >>>
> >> >>> On Sun, Nov 8, 2009 at 10:11 AM, Duncan Murdoch
<murdoch@...>
> >> >>> wrote:
> >> >>>>
> >> >>>> On 08/11/2009 11:03 AM, Peng Yu wrote:
> >> >>>>>
> >> >>>>> I'm wondering which textbook discussed the various contrast
matrices
> >> >>>>> mentioned in the help page of 'contr.helmert'. Could somebody let
me

> >> >>>>> know?
> >> >>>>
> >> >>>> Doesn't the reference on that page discuss them?
> >> >>>
> >> >>> It does explain what the functions are. But I need a more basic and
> >> >>> complete reference. For example, I want to understand what 'Helmert
> >> >>> parametrization' (on page 33 of 'Statistical Models in S') is.
> >> >>>
> >> >>
> >> >> Just google for: Helmert contrasts
> >> >
> >> > Or,
> >> >
> >> >> contr.helmert(5)
> >> > [,1] [,2] [,3] [,4]
> >> > 1 -1 -1 -1 -1
> >> > 2 1 -1 -1 -1
> >> > 3 0 2 -1 -1
> >> > 4 0 0 3 -1
> >> > 5 0 0 0 4
> >> >
> >> >> MASS::fractions(MASS::ginv(contr.helmert(5)))
> >> > [,1] [,2] [,3] [,4] [,5]
> >> > [1,] -1/2 1/2 0 0 0
> >> > [2,] -1/6 -1/6 1/3 0 0
> >> > [3,] -1/12 -1/12 -1/12 1/4 0
> >> > [4,] -1/20 -1/20 -1/20 -1/20 1/5
> >> >
> >> > and apply brains.
> >> >
> >> > I.e., except for a slightly odd multiplier, the parameters represent

the
> >> > difference between each level and the average of the preceding
levels.

> >>
> >> I realized that my questions are what a contrast matrix is and how it
> >> is related to hypothesis testing. For a give hypothesis, how to get
> >> the corresponding contrast matrix in a systematical way? There are
> >> some online materials, but they are all diffused. I have also read the
> >> book Applied Linear Regression Models, which doesn't give a complete
> >> descriptions on all the aspects of contrast and contrast matrix. But I
> >> would want a textbook that gives a complete description, so that I
> >> don't have to look around for other materials.
> >>
> >> ______________________________________________
> >> R-help@... 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.
> >
> >
> >
>
> ______________________________________________
> R-help@... 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.

______________________________________________
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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.

From forum: R help

Re: Outputing multilple subsets

2009-11-08T17:31:50Z

On Nov 8, 2009, at 7:23 PM, rusers.sh wrote:

> for (i in num) {
> c_num<-c[c$b==num,]
> write.csv(c_num,file="c:/c_num.csv")
> }
>
> Warning messages:
> 1: In c$b == num :
> longer object length is not a multiple of shorter object length

This is because you're comparing column b to the entire vector of
numbers (num), not the current number in the iteration (i). The first
line of the loop should be "c_num<-c[c$b==i,]".

From a style point of view, I'd use "n" as my variable, since "i" is
too commonly used as an integer index.

Also, you will be overwriting the same file, called "c_num.csv", on
each iteration.

You should try something more like:

for (n in num) {
c.n <- c[c$b==n,]
write.csv(c.n, file=paste("c:/c_", n, ".csv", sep="")
}

I hope that helps.

Cheers,
Johann Hibschman

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From forum: R help

Re: Models for Discrete Choice in R

2009-11-08T17:05:29Z

Emmanuel Charpentier wrote:

> Le dimanche 08 novembre 2009 à 17:07 -0200, Iuri Gavronski a écrit :
>> Hi,
>>
>> I would like to fit Logit models for ordered data, such as those
>> suggested by Greene (2003), p. 736.
>>
>> Does anyone suggests any package in R for that?
>
> look up the polr function in package MASS (and read the relevant pages
> in V&R4 and some quoted references...) or the slightly more
> sophisticated (larger range of models) lrm function in F. Harrell's
> Design (now rms) packge (but be aware that Design is a huge beast witch
> carries its own "computing universe", based on (strong) Harrell's view
> of what a regression analysis should be : reading his book is, IMHO,
> necessary to understand his choices and agree (or disgree) with them).
>
> If you have a multilevel model (a. k. a. one "random effect" grouping),
> the "repolr" packge aims at that, but I've been unable to use it
> recently (numerical exceptions).
>
>> By the way, my dependent variable is ordinal and my independent
>> variables are ratio/intervalar.
>
> Numeric ? Then maybe some recoding/transformation is in order ... in
> which case Design/rms might or might not be useful.

I'm not clear on what recoding or transformation is needed for an
ordinal dependent variable and ratio/interval independent variables, nor
why rms/Design would not be useful.

Frank

>
> HTH,
>
> Emmanuel Charpentier

--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University

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From forum: R help

Re: Outputing multilple subsets

2009-11-08T16:53:31Z

Have you considered using split?

-Ista

On Sun, Nov 8, 2009 at 7:23 PM, rusers.sh <rusers.sh@...> wrote:

> Hi Rusers,
> I hope to divide the original dataset into several subsets and output
> these multilple datasets. But errors appeared in my loops. See example.
> ######
> a<-c(1:10)
> b<-c(rep(1,3),rep(2,3),rep(3,4))
> c<-data.frame(a,b) #c is the example data
> num<-c(unique(b))
> # I hope to get the subsets c_1.csv,c_2.csv and c_3.csv
> #Errors
> for (i in num) {
> c_num<-c[c$b==num,]
> write.csv(c_num,file="c:/c_num.csv")
> }
>
> Warning messages:
> 1: In c$b == num :
> longer object length is not a multiple of shorter object length
> 2: In c$b == num :
> longer object length is not a multiple of shorter object length
> 3: In c$b == num :
> longer object length is not a multiple of shorter object length
> I think the problem should be file="c:/c_num.csv", anybody has ever met
> this problem?
> Thanks very much.
> -----------------
> Jane Chang
> Queen's
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@... 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.
>

--
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org

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and provide commented, minimal, self-contained, reproducible code.

From forum: R help

Re: Simple 2-Way Anova issue in R

2009-11-08T16:42:45Z

> x
Year Depth Biomass1 Biomass2
1 1999 10 14.3 14.7
2 1999 15 14.7 15.6
> require(reshape)
> melt(x, id=c('Year','Depth'))
Year Depth variable value
1 1999 10 Biomass1 14.3
2 1999 15 Biomass1 14.7
3 1999 10 Biomass2 14.7
4 1999 15 Biomass2 15.6

On Sun, Nov 8, 2009 at 7:38 PM, znd <zackdaugherty@...> wrote:

>
> Ah, I believe I constructed my *.csv wrong in that I only had 1 observation
> within groups whereas I needed at least 2.
>
> Originally I had:
>
> Year Depth Biomass1 Biomass2
> 1999 10 14.3 14.7
> 1999 15 14.7 15.6
> etc.
>
> but I switched this to:
>
> Year Depth Biomass
> 1999 10 14.3
> 1999 10 14.7
> 1999 15 15.1
> 1999 15 15.6
>
> I believe this may be the appropriate way to collapse my biomass data in
> order to perform a 2-way anova.
>
> If not feel free comment and thanks for the help.
> --
> View this message in context: http://old.nabble.com/Simple-2-Way-Anova-issue-in-R-tp26258684p26259649.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@... 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.
>

From forum: R help

Re: reference on contr.helmert and typo on its help page.

2009-11-08T16:41:15Z

Dear John,

I did read Section 9.1.2 and various other textbooks before posting my
questions. But each reference uses slightly different notations and
terminology. I get confused and would like a description that
summaries everything so that I don't have to refer to many different
resources. May I ask a few questions on the section in your textbook?

Which variable in Section 9.1.2 is "a matrix of contrasts" mentioned
in the help page of 'contr.helmert'? Which matrix of contrast in R
corresponds to dummy regression? With different R formula, e.g. y ~ x
vs. y ~ x -1, $X_F$ (mentioned on page 189) is different and hence
$\beta_F$ (mentioned in eq. 9.3) is be different. So my understanding
is that the matrix of contrast should depend on the formula. But it is
not according to the help page of "contr.helmert".

Regards,
Peng

On Sun, Nov 8, 2009 at 6:17 PM, John Fox <jfox@...> wrote:

> Dear Peng Yu,
>
> Perhaps you're referring to my text, Applied Linear Regression Analysis and
> Generalized Linear Models, since I seem to recall that you sent me a number
> of questions about it. See Section 9.1.2 on linear contrasts for the answer
> to your question.
>
> I hope this helps,
> John
>
> --------------------------------
> John Fox
> Senator William McMaster
> Professor of Social Statistics
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> web: socserv.mcmaster.ca/jfox
>
>
>> -----Original Message-----
>> From: r-help-bounces@... [mailto:r-help-bounces@...]
> On
>> Behalf Of Peng Yu
>> Sent: November-08-09 4:52 PM
>> To: r-help@...
>> Subject: Re: [R] reference on contr.helmert and typo on its help page.
>>
>> On Sun, Nov 8, 2009 at 11:28 AM, Peter Dalgaard
>> <p.dalgaard@...> wrote:
>> > Gabor Grothendieck wrote:
>> >>
>> >> On Sun, Nov 8, 2009 at 11:59 AM, Peng Yu <pengyu.ut@...> wrote:
>> >>>
>> >>> On Sun, Nov 8, 2009 at 10:11 AM, Duncan Murdoch <murdoch@...>
>> >>> wrote:
>> >>>>
>> >>>> On 08/11/2009 11:03 AM, Peng Yu wrote:
>> >>>>>
>> >>>>> I'm wondering which textbook discussed the various contrast matrices
>> >>>>> mentioned in the help page of 'contr.helmert'. Could somebody let me
>> >>>>> know?
>> >>>>
>> >>>> Doesn't the reference on that page discuss them?
>> >>>
>> >>> It does explain what the functions are. But I need a more basic and
>> >>> complete reference. For example, I want to understand what 'Helmert
>> >>> parametrization' (on page 33 of 'Statistical Models in S') is.
>> >>>
>> >>
>> >> Just google for: Helmert contrasts
>> >
>> > Or,
>> >
>> >> contr.helmert(5)
>> > [,1] [,2] [,3] [,4]
>> > 1 -1 -1 -1 -1
>> > 2 1 -1 -1 -1
>> > 3 0 2 -1 -1
>> > 4 0 0 3 -1
>> > 5 0 0 0 4
>> >
>> >> MASS::fractions(MASS::ginv(contr.helmert(5)))
>> > [,1] [,2] [,3] [,4] [,5]
>> > [1,] -1/2 1/2 0 0 0
>> > [2,] -1/6 -1/6 1/3 0 0
>> > [3,] -1/12 -1/12 -1/12 1/4 0
>> > [4,] -1/20 -1/20 -1/20 -1/20 1/5
>> >
>> > and apply brains.
>> >
>> > I.e., except for a slightly odd multiplier, the parameters represent the
>> > difference between each level and the average of the preceding levels.
>>
>> I realized that my questions are what a contrast matrix is and how it
>> is related to hypothesis testing. For a give hypothesis, how to get
>> the corresponding contrast matrix in a systematical way? There are
>> some online materials, but they are all diffused. I have also read the
>> book Applied Linear Regression Models, which doesn't give a complete
>> descriptions on all the aspects of contrast and contrast matrix. But I
>> would want a textbook that gives a complete description, so that I
>> don't have to look around for other materials.
>>
>> ______________________________________________
>> R-help@... 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.
>
>
>

From forum: R help

Re: Simple 2-Way Anova issue in R

2009-11-08T16:38:15Z

Ah, I believe I constructed my *.csv wrong in that I only had 1 observation within groups whereas I needed at least 2.

Originally I had:

Year Depth Biomass1 Biomass2
1999 10 14.3 14.7
1999 15 14.7 15.6
etc.

but I switched this to:

Year Depth Biomass
1999 10 14.3
1999 10 14.7
1999 15 15.1
1999 15 15.6

I believe this may be the appropriate way to collapse my biomass data in order to perform a 2-way anova.

If not feel free comment and thanks for the help.

From forum: R help

Outputing multilple subsets

2009-11-08T16:23:43Z

Hi Rusers,
I hope to divide the original dataset into several subsets and output
these multilple datasets. But errors appeared in my loops. See example.
######
a<-c(1:10)
b<-c(rep(1,3),rep(2,3),rep(3,4))
c<-data.frame(a,b) #c is the example data
num<-c(unique(b))
# I hope to get the subsets c_1.csv,c_2.csv and c_3.csv
#Errors
for (i in num) {
c_num<-c[c$b==num,]
write.csv(c_num,file="c:/c_num.csv")
}

Warning messages:
1: In c$b == num :
longer object length is not a multiple of shorter object length
2: In c$b == num :
longer object length is not a multiple of shorter object length
3: In c$b == num :
longer object length is not a multiple of shorter object length
I think the problem should be file="c:/c_num.csv", anybody has ever met
this problem?
Thanks very much.
-----------------
Jane Chang
Queen's

[[alternative HTML version deleted]]

______________________________________________
R-help@... 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.

From forum: R help

Re: reference on contr.helmert and typo on its help page.

2009-11-08T16:17:59Z

Dear Peng Yu,

Perhaps you're referring to my text, Applied Linear Regression Analysis and
Generalized Linear Models, since I seem to recall that you sent me a number
of questions about it. See Section 9.1.2 on linear contrasts for the answer
to your question.

I hope this helps,
John

--------------------------------
John Fox
Senator William McMaster
Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
web: socserv.mcmaster.ca/jfox

> -----Original Message-----
> From: r-help-bounces@... [mailto:r-help-bounces@...]
On

> Behalf Of Peng Yu
> Sent: November-08-09 4:52 PM
> To: r-help@...
> Subject: Re: [R] reference on contr.helmert and typo on its help page.
>
> On Sun, Nov 8, 2009 at 11:28 AM, Peter Dalgaard
> <p.dalgaard@...> wrote:
> > Gabor Grothendieck wrote:
> >>
> >> On Sun, Nov 8, 2009 at 11:59 AM, Peng Yu <pengyu.ut@...> wrote:
> >>>
> >>> On Sun, Nov 8, 2009 at 10:11 AM, Duncan Murdoch <murdoch@...>
> >>> wrote:
> >>>>
> >>>> On 08/11/2009 11:03 AM, Peng Yu wrote:
> >>>>>
> >>>>> I'm wondering which textbook discussed the various contrast matrices
> >>>>> mentioned in the help page of 'contr.helmert'. Could somebody let me
> >>>>> know?
> >>>>
> >>>> Doesn't the reference on that page discuss them?
> >>>
> >>> It does explain what the functions are. But I need a more basic and
> >>> complete reference. For example, I want to understand what 'Helmert
> >>> parametrization' (on page 33 of 'Statistical Models in S') is.
> >>>
> >>
> >> Just google for: Helmert contrasts
> >
> > Or,
> >
> >> contr.helmert(5)
> > [,1] [,2] [,3] [,4]
> > 1 -1 -1 -1 -1
> > 2 1 -1 -1 -1
> > 3 0 2 -1 -1
> > 4 0 0 3 -1
> > 5 0 0 0 4
> >
> >> MASS::fractions(MASS::ginv(contr.helmert(5)))
> > [,1] [,2] [,3] [,4] [,5]
> > [1,] -1/2 1/2 0 0 0
> > [2,] -1/6 -1/6 1/3 0 0
> > [3,] -1/12 -1/12 -1/12 1/4 0
> > [4,] -1/20 -1/20 -1/20 -1/20 1/5
> >
> > and apply brains.
> >
> > I.e., except for a slightly odd multiplier, the parameters represent the
> > difference between each level and the average of the preceding levels.
>
> I realized that my questions are what a contrast matrix is and how it
> is related to hypothesis testing. For a give hypothesis, how to get
> the corresponding contrast matrix in a systematical way? There are
> some online materials, but they are all diffused. I have also read the
> book Applied Linear Regression Models, which doesn't give a complete
> descriptions on all the aspects of contrast and contrast matrix. But I
> would want a textbook that gives a complete description, so that I
> don't have to look around for other materials.
>
> ______________________________________________
> R-help@... mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide

From forum: R help