Nabble - R help

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

2009-11-10T01:08:19Z

On Mon, 2009-11-09 at 19:31 -0600, Peng Yu wrote:

> On Sun, Nov 8, 2009 at 7:32 PM, John Fox <jfox@...> wrote:
> > Dear Peng,
> >
> > I'm tempted to try to get an entry in the fortunes package but will instead
> > try to answer your questions directly:
>
> I can not install 'fortunes'. What are the fortunes packages about?
> > install.packages("fortunes", repos="http://R-Forge.R-project.org")
> Warning: unable to access index for repository
> http://R-Forge.R-project.org/bin/macosx/leopard/contrib/2.9
> Warning message:
> In getDependencies(pkgs, dependencies, available, lib) :
> package ‘fortunes’ is not available

It is on CRAN. just do:

install.packages("fortunes")

Choose a repository near to you and it should install.

There appears to be a problem with R-forge and Mac OsX binaries. The
code you show above works for me on linux and R2.9.x

G

>
> >> 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.
>
> What is the mathematical definition of polynomial contrasts? Why
> polynomial contrasts are the default contrasts for ordered factors?
>
> ______________________________________________
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> 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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p-generalized normal distribution

2009-11-10T00:45:52Z

Hello,

I would like to know if there is already an R-package available for
computing the density, distribution function, quantiles and random
numbers of the p-generalized normal distribution.

Best regards,
Steve Kalke

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Odp: creating multiple plots using a splitting factor

2009-11-09T23:30:40Z

Hi

r-help-bounces@... napsal dne 10.11.2009 01:01:06:

> Hello,
>
>
>
> I am new to R. I often collect data at multiple sites and need to
> create separate graphs (such as scatterplots or histograms) of specific
> variables for each site. I have tried to do this by splitting the data

You could also consider lattice and/or ggplot2 packages for that.

Regards
Petr

> frame and then using lapply, but it seems that the graphing commands
> cannot be called as functions. Here is a sample of my data, called
> "seeddist2":
>
>
>
> site DaysSinceRelease distance_cm
>
> 10 GVM 1 17.8
>
> 11 GVM 1 17.8
>
> 12 GVM 1 14.0
>
> 13 GVM 1 14.0
>
> 14 GVM 1 14.9
>
> 15 GVM 1 25.4
>
> 16 WRR 1 25.4
>
> 17 WRR 1 35.0
>
> 18 WRR 1 45.0
>
> 19 WRR 1 55.0
>
> 20 WRR 1 60.0
>
>
>
> Here is what I tried to get separate histograms of distance_cm by site:
>
> splitlist<- split(seeddist2, site)
>
> lapply(splitlist, hist(distance_cm, breaks=10))
>
>
>
> I then get an error message saying that "match.fun" didn't find the
> function. Is there another way to produce multiple graphs at once?
>
>
>
> Thank you,
>
>
>
> Danielle B. Johnston, Habitat Researcher
>
> Colorado Division of Wildlife
>
>
>
>
> [[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.

______________________________________________
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Re: Complicated For Loop (to me)

2009-11-09T23:27:06Z

Hi

You probably may use some of aggregate functions (by, tapply, aggregate)

aggregate(some.columns.of.data frame, list(SLUNCH, ETHNIC, RACE,
DIVISION), function(x) x/sum(x))

Untested on your data.

Regards
Petr

r-help-bounces@... napsal dne 10.11.2009 03:51:55:

>
> Sorry, I've been trying to work around this and just got back to check
my
> email.
>
> dput wasn't working too well for me because the data set also has 450
> variables and I needed more time to figure out how to properly show you
all

> what you needed to know.
>
> But to show you the idea, a very simple data set would be:
>
> NWEIGHT ETHNIC RACE SLUNCH DIVISION .......
> 1234 0 1 1 1
> 2345 1 1 0 5
> 3243 0 3 1 3
> . . . . .
> . . . . .
> . . . . .
> . . . . .
>
>
> So basically, I already have the data subset by division and race. (I

did
> that the inefficient way by coding it by hand)
>
> But now I need to calculate the percentage of each division (by race)
that

> participates in SLUNCH (a 0 1 variable)
>
> So I am trying to avoid writing out code such as:
>
> w.cd1.s <- sum(ifelse(white.cd1$SLUNCH==1, white.cd1$NWEIGHT,
> 0))/sum(white.cd1$NWEIGHT)
> w.cd2.s <- sum(ifelse(white.cd2$SLUNCH==1, white.cd2$NWEIGHT,
> 0))/sum(white.cd2$NWEIGHT)
> .... for all the variables.
>
> One other method that I tried, which gets me the "names" i need, but

doesn't
> put them into a dataframe (which I am currently trying to fix) is by
using

> this code:
>
>
> names <- c("white","black","hispanic","asian")
> regions <- c("cd1","cd2","cd3","cd4","cd5","cd6","cd7","cd8","cd9")
> type <- c("l", "p", "r")
> name.region <- c()
> for (j in 1:length(names)){
> for(i in 1:length(regions)){
> for(k in 1:length(type)){
> name.holder <- paste(names[j],".",paste(regions[i],".", type[k],

sep=""),
> sep="")
> name.region <- c(name.region, name.holder)
> }
> }
> }
>
> (The "l", "p", "r" represent other variables that I am trying to do the
same
> thing as SLUNCH)
>
> >From here I've been trouble-shooting how to switch these named
variables
> back into a data.frame context.
>
> Everyone's help has been really appreciated! I've learned a lot today
that
> will hopefully move me slowly from using for loops to more efficient
> functions. I unfortunately am still learning those and have some
knowledge
> about how to use loops compared to almost no knowledge of the more
powerful
> functions like sapply, lapply, etc. (I'm waiting on MASS4 to be
returned to
> the library to read it.)
>
>
> Thanks!
>
>
> John Kane-2 wrote:
> >
> > I think that we probably need a sample database of your original data.

> > A few lines of the dataset would probably be enough as long as it was
> > fairly representative of the overall data set. See ?dput for a way of
> > conveniently supply a sample data set.
> >
> > Otherwise off the top of my head, I would think that you could just
put

> > all your subsets into a list and use lapply but I'm simply guessing
> > without seeing the data.
> >
> > --- On Mon, 11/9/09, agm. <amurray@...> wrote:
> >
> >> From: agm. <amurray@...>
> >> Subject: Re: [R] Complicated For Loop (to me)
> >> To: r-help@...
> >> Received: Monday, November 9, 2009, 3:18 PM
> >>
> >> I've looked through ?split and run all of the code, but I
> >> am not sure that I
> >> can use it in such a way to make it do what I need.
> >> Another suggestion was
> >> using "lists", but again, I am sure that the process can do
> >> what I need, but
> >> I am not sure it would work with so many observations.
> >>
> >> I might have been too simple in my code. Let me try
> >> to explain it more
> >> clearly:
> >>
> >> I've got a data set of 4500 observations. I have
> >> already subset it into
> >> race/ethnicity (which I did by simple code). Now I
> >> needed to subset each
> >> race/ethnicity again into 9 separate regions. I again
> >> did this by simple
> >> code.
> >>
> >> The problem is now, I need to calculate a percentage for
> >> three different
> >> variables for all 9 regions for each race. I was
> >> trying to do this through
> >> a loop command.
> >>
> >> So a snippet of my code is :
> >>
> >> names <- c("white", "black", "asian", "hispanic")
> >> for(j in 1:length(names)){
> >> for(i in 1:9){
> >> names[j].cd[i].es.wash <- subset(names[j].cd[i],
> >> SLUNCH==1)
> >> es.cd[i].names.w <-
> >> sum(names.cd[i].es.wash$NWEIGHT)/sum(names.cd[i]$NWEIGHT)
> >> }
> >> }
> >>
> >>
> >> Maybe that makes it clearer. If not, I
> >> apologize. Thanks for the help that
> >> I have already received. It is greatly appreciated.
> >>
> >> Tony
> >>
> >> --
> >> View this message in context:
> >>

http://old.nabble.com/Complicated-For-Loop-%28to-me%29-tp26269479p26272994.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.
> >>
> >
> >
> > __________________________________________________________________
> > Make your browsing faster, safer, and easier with the new Internet
> > Explorer® 8. Optimized for Yahoo! Get i
> > ______________________________________________
> > 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.
> >
> >
>
> --
> View this message in context:

http://old.nabble.com/Complicated-For-Loop-%
> 28to-me%29-tp26269479p26277512.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.

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

Command-line arguments and --interactive

2009-11-09T22:52:56Z

Hello,

I am interested in passing a command or two to R on the command
line. The desired behavior is for R to run these commands first, and then
begin an interactive session. For example:

$ R -e 'foo <- read.csv("/tmp/foo.csv")'
...which would launch R and execute that command, so when I return from
getting coffee, foo would be loaded.

I know that if I put this command into my .Rprofile, it will execute as
desired, but I'm looking for something a bit more modular.

I have tried the following:

$ echo '10*5' | R
...fails unless I specify --save --no-save or --vanilla. I choose the
latter; R prints 50 and quits...but I do not want it to quit!

$ echo '10*5' | R --interactive

...fails in an interesting way: I get an infinite loop of Save workspace
image? [y/n/c]: Save workspace image? [y/n/c]:, etc. I have to kill R in
another window. This also happens with
$ echo '10*5; scan()' | R --interactive

...even though I'd expect scan() to prompt for input. If I specify

$ echo '10*5; scan()' | R --interactive --vanilla

...then R just prints 50 and quits. Is this a bug with "interactive?" The
description of "Force an interactive session" is not informative enough for
me to have any further guess as to what to do here, but my expectation is
that it would make R not auto-quit.

...perhaps I don't know how to use --interactive properly? Could somebody
point me on the right track? Googling for "R --interactive" is nigh untu
useless. :-\

This behavior is present in R 2.9.2 and 2.10.0.

Many thanks!

Cordially,
Adam Kramer

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Re: Hand-crafting an .RData file

2009-11-09T22:31:39Z

Thanks as always for a very helpful response. I'm now loading a few million
rows in only a few seconds.

Cordially,
Adam Kramer

On Mon, 9 Nov 2009, Prof Brian Ripley wrote:

> 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
>
> ______________________________________________
> 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.

How to remove/prevent trailing space after tab completion in R shell

2009-11-09T21:39:33Z

Hi -

Not a mission critical issue, but still highly annoying. I just upgraded R
to 2.10.0 (the binary for Ubuntu karmic) and the tab completion facility now
inserts a space after every completed term (something it didn't do in 2.9.0
or 2.9.2). It wouldn't be an issue so much if it weren't for the fact that
it screws up tab-completing the next term, i.e. if I have a list of lists of
something else, i.e.
tmp.list <- list()
tmp.list[[ "An.example" ]] <- list()
tmp.list[[ "An.example" ]][[ "mat" ]] <- matrix( 10, nr=10, nc=1 )

using tab-completion to fill in the 'tmp.list$An.example" will result in a
trailing space after the "$An.example" - "$An.example* *", requiring that it
be deleted before the next term is specified.

Any idea how to turn this off? Is this a shell integration issue?
Something I'd need to do by compiling my own version?

Thanks!

Peter Waltman

[[alternative HTML version deleted]]

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how to compute Autocorrelation function use fft

2009-11-09T21:22:26Z

Dear all£¬
Could you tell me how to compute Autocorrelation function use fft?
Thanks!
[[alternative HTML version deleted]]

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how to create time series object

2009-11-09T21:17:20Z

Dear all,
Could you tell me how to create time series object,
time=c('2009-10-1','2009-10-3','2009-10-4'...)
data=c(124,231,240...)

Thanks!
[[alternative HTML version deleted]]

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Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T20:35:11Z

Thank you very much for all your help. This helped a lot. Very
constructive input.

Sincerely,

Sergios Charntikov (Sergey), MA

Behavioral Neuropharmacology Lab
Department of Psychology
University of Nebraska-Lincoln
Lincoln, NE 68588-0308 USA

On Mon, Nov 9, 2009 at 9:53 PM, Ista Zahn <istazahn@...> wrote:

> Yes, reshaping data is straightforward in R. No need to copy/paste in
> a spreadsheet.
> See ?reshape and/or the melt/cast functions in the reshape package.
>
> -Ista
>
> On Mon, Nov 9, 2009 at 9:20 PM, Sergios (Sergey) Charntikov
> <sergioschr@...> wrote:
>> Thank you very much. Finally got it to work. However, I had to recode it from:
>> columns: subject/treatment/DV (where all my response data was in one
>> DV column) to columns: subject/treatment/day1/day2/day3/ (where my
>> response data is now in three different columns).
>>
>> Is there a way to do that without hand recoding (cutting and pasting
>> in spreadsheet) by hand? Thank you for your help. Glad it works as
>> is.
>>
>>
>> Sincerely,
>>
>> Sergios Charntikov (Sergey), MA
>>
>> Behavioral Neuropharmacology Lab
>> Department of Psychology
>> University of Nebraska-Lincoln
>> Lincoln, NE 68588-0308 USA
>>
>>
>>
>>
>>
>> On Mon, Nov 9, 2009 at 7:12 PM, John Fox <jfox@...> wrote:
>>> Dear Sergios,
>>>
>>> Why don't you try what I suggested originally? Adapted to this data set,
>>>
>>> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>>> idata <- data.frame(Day=factor(1:3))
>>> summary(Anova(mod, idata=idata, idesign=~Day))
>>>
>>> Peter Dalgaard also pointed toward an article that describes how to do the
>>> same thing with anova().
>>>
>>> Regards,
>>> John
>>>
>>>> -----Original Message-----
>>>> From: r-help-bounces@... [mailto:r-help-bounces@...]
>>> On
>>>> Behalf Of Sergios (Sergey) Charntikov
>>>> Sent: November-09-09 7:13 PM
>>>> To: Mike Lawrence
>>>> Cc: r-help@...
>>>> Subject: Re: [R] Getting Sphericity Tests for Within Subject Repeated
>>> Measure
>>>> Anova (using "car" package)
>>>>
>>>> Hi Mike,
>>>>
>>>> I tried to run my data in SPSS and it works fine without any problems,
>>> plug
>>>> in my levels, plug in my covariate (since it is all within) and get my
>>>> Mauchly Tests.
>>>>
>>>> I tried to rearrange the data so it looks like this
>>>>
>>>> subj/treatment/day1/day2/day3
>>>>
>>>> subject    treatment    day1    day2    day3
>>>> 1    1    8    8    8
>>>> 1    2    5    7    5
>>>> 2    1    7    4    4
>>>> 2    2    4    5    7
>>>> 3    1    8    6    4
>>>> 3    2    5    2    4
>>>> 4    1    2    9    4
>>>> 4    2    1    9    1
>>>> 5    1    4    8    1
>>>> 5    2    7    8    2
>>>> 6    1    4    7    2
>>>> 6    2    4    5    2
>>>>
>>>>
>>>> When I try mlmfit <- lm(Dataset~1), I get "invalid type (list) for
>>> variable
>>>> 'Dataset"
>>>>
>>>> When I try
>>>>
>>>> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>>>>
>>>> idata<- data.frame(factor(rep(c(Dataset$day1, Dataset$day2,
>>> Dataset$day3))),
>>>> ordered(Dataset$Treatment))
>>>>
>>>> Anova(mod, idata=idata, idesign=~Dataset$Treatment)
>>>>
>>>> I get: Terms in the intra-subject model matrix are not orthogonal.
>>>>
>>>> When I try is.matrix(Dataset) - I get no.
>>>>
>>>> My original mock Dataset (attached in txt) is below. Maybe I am not
>>> coding
>>>> it right? I would hate to recode all my data for SPSS, since at the end I
>>>> would need to show that Sphericity was not violated.
>>>>
>>>> Subj Trtmt Sessn Response
>>>>
>>>> 1 N 1 5
>>>>
>>>> 1 D 1 6
>>>>
>>>> 1 N 2 4
>>>>
>>>> 1 D 2 7
>>>>
>>>> 2 N 1 8
>>>>
>>>> 2 D 1 9
>>>>
>>>> 2 N 2 2
>>>>
>>>> 2 D 2 1
>>>>
>>>> 3 N 1 4
>>>>
>>>> 3 D 1 5
>>>>
>>>> 3 N 2 6
>>>>
>>>> 3 D 2 2
>>>>
>>>> 4 N 1 5
>>>>
>>>> 4 D 1 6
>>>>
>>>> 4 N 2 4
>>>>
>>>> 4 D 2 7
>>>>
>>>> 5 N 1 8
>>>>
>>>> 5 D 1 9
>>>>
>>>> 5 N 2 2
>>>>
>>>> 5 D 2 1
>>>>
>>>> 6 N 1 4
>>>>
>>>> 6 D 1 5
>>>>
>>>> 6 N 2 6
>>>>
>>>> 6 D 2 2
>>>>
>>>>
>>>>
>>>>
>>>> Sincerely,
>>>>
>>>> Sergios Charntikov (Sergey), MA
>>>>
>>>> Behavioral Neuropharmacology Lab
>>>> Department of Psychology
>>>> University of Nebraska-Lincoln
>>>> Lincoln, NE 68588-0308 USA
>>>>
>>>>
>>>>
>>>> On Mon, Nov 9, 2009 at 5:29 PM, Mike Lawrence <Mike.Lawrence@...>
>>> wrote:
>>>> >
>>>> > No luck as in...? What error did you encounter?
>>>> >
>>>> > In your example data set, you only have 2 levels of each within-Ss
>>>> > factor, in which case you shouldn't expect to obtain tests of
>>>> > sphericity; as far as I understand it, sphericity necessarily holds
>>>> > when for repeated measures with only 2 levels and tests are really
>>>> > only possible for repeated measures with 3 or more levels.
>>>> >
>>>> > I think it's analogous to how you don't need to test homogeneity of
>>>> > variance when performing a paired t-test; the test ends up
>>>> > representing the pairs as single distribution of difference scores
>>>> > with a single variance.
>>>> >
>>>> > Mike
>>>> >
>>>> > On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
>>>> > <sergioschr@...> wrote:
>>>> > > Tried EZanova, no luck with my particular dataset.
>>>> > >
>>>> > >
>>>> > > Sincerely,
>>>> > >
>>>> > > Sergios Charntikov (Sergey), MA
>>>> > >
>>>> > > Behavioral Neuropharmacology Lab
>>>> > > Department of Psychology
>>>> > > University of Nebraska-Lincoln
>>>> > > Lincoln, NE 68588-0308 USA
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > > On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...>
>>>> wrote:
>>>> > >>
>>>> > >> Have you tried ezANOVA from the ez pacakge? It attempts to provide
>>>> > >> a simple user interface to car's ANOVA (and when that fails, aov).
>>>> > >>
>>>> > >> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
>>>> > >> <sergioschr@...> wrote:
>>>> > >> > Hello everyone,
>>>> > >> >
>>>> > >> > I am trying to do within subjects repeated measures anova
>>>> > >> > followed by the test of sphericity (sample dataset below).
>>>> > >> > I am able to get either mixed model or linear model anova and
>>>> > >> > TukeyHSD, but have no luck with Repeated-Measures Assuming
>>>> > >> > Sphericity or Separate Sphericity Tests.
>>>> > >> > I am trying to follow example from "car" package, but it seems
>>>> > >> > that I am not getting something right.
>>>> > >> >
>>>> > >> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
>>>> > >> >
>>>> > >> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
>>>> > >> >
>>>> > >> >> summary(LinearModel.1)
>>>> > >> >
>>>> > >> > All, good so far, but I have problem understanding "idata=" and
>>>> > >> > "idesign="
>>>> > >> > functions pertaining to my example. Session is my repeated
>>>> > >> > measure (Sessn 1 and Sessn 2 = two sessions, in reality I have
>>>> > >> > more) and it is already stacked. Any help or guidance on this
>>>> > >> > matter.
>>>> > >> >
>>>> > >> > Thank you, my mock dataset is below. Each subject has two levels
>>>> > >> > of treatment throughout four calendar days which are recoded to
>>>> > >> > Session 1 and Session 2 in order to compare treatments by the
>>>> > >> > first and subsequent days of exposure (Treatment x Session; my DV
>>>> > >> > is Response; Session is repeated).
>>>> > >> >
>>>> > >> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7
>>>> > >> > 2 N 1
>>>> > >> > 8
>>>> > >> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4
>>>> > >> > N 1 5
>>>> > >> > 4 D
>>>> > >> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1
>>>> > >> > 4 6 D
>>>> > >> > 1 5
>>>> > >> > 6 N 2 6 6 D 2 2
>>>> > >> >
>>>> > >> > Sincerely,
>>>> > >> >
>>>> > >> > Sergios Charntikov (Sergey), MA
>>>> > >> >
>>>> > >> > Behavioral Neuropharmacology Lab
>>>> > >> > Department of Psychology
>>>> > >> > University of Nebraska-Lincoln
>>>> > >> > Lincoln, NE 68588-0308 USA
>>>> > >> >
>>>> > >> > sergioschr-at-gmail-dot-com
>>>> > >> >
>>>> > >> > [[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.
>>>> > >> >
>>>> > >> >
>>>> > >>
>>>> > >>
>>>> > >>
>>>> > >> --
>>>> > >> 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. ~
>>>> > >
>>>> > >
>>>> >
>>>> >
>>>> >
>>>> > --
>>>> > 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. ~
>>>
>>>
>>>
>>
>> ______________________________________________
>> 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
>

Re: Complicated For Loop (to me)

2009-11-09T20:00:48Z

On Nov 9, 2009, at 9:51 PM, agm. wrote:

>
> Sorry, I've been trying to work around this and just got back to
> check my
> email.
>
> dput wasn't working too well for me because the data set also has 450
> variables and I needed more time to figure out how to properly show
> you all
> what you needed to know.

That's not a very convincing story. Nabble lets you put files where
they can be accessed. There are examples of nabble users doing that
today.

> But to show you the idea, a very simple data set would be:
>
> NWEIGHT ETHNIC RACE SLUNCH DIVISION .......
> 1234 0 1 1 1
> 2345 1 1 0 5
> 3243 0 3 1 3
> . . . . .
> . . . . .
> . . . . .
> . . . . .
>
> So basically, I already have the data subset by division and race.
> (I did
> that the inefficient way by coding it by hand)

Probably did not not need to do that:

?split

>
> But now I need to calculate the percentage of each division (by
> race) that
> participates in SLUNCH (a 0 1 variable)

?tapply
?by

> So I am trying to avoid writing out code such as:
>
> w.cd1.s <- sum(ifelse(white.cd1$SLUNCH==1, white.cd1$NWEIGHT,
> 0))/sum(white.cd1$NWEIGHT)

Perhaps:

by(white.cd1$NMEIGHT, white.cd1$SLUNCH, sum, na.rm=TRUE)/
sum(white.cd1$NWEIGHT, na.rm=TRUE)

> w.cd2.s <- sum(ifelse(white.cd2$SLUNCH==1, white.cd2$NWEIGHT,
> 0))/sum(white.cd2$NWEIGHT)

> .... for all the variables.

?apply
?lapply

> One other method that I tried, which gets me the "names" i need, but
> doesn't
> put them into a dataframe (which I am currently trying to fix) is by
> using
> this code:
>
>
> names <- c("white","black","hispanic","asian")
> regions <- c("cd1","cd2","cd3","cd4","cd5","cd6","cd7","cd8","cd9")
> type <- c("l", "p", "r")
> name.region <- c()
> for (j in 1:length(names)){
> for(i in 1:length(regions)){
> for(k in 1:length(type)){
> name.holder <- paste(names[j],".",paste(regions[i],".", type[k],
> sep=""),
> sep="")
> name.region <- c(name.region, name.holder)
> }
> }
> }
>
> (The "l", "p", "r" represent other variables that I am trying to do
> the same
> thing as SLUNCH)
>
>> From here I've been trouble-shooting how to switch these named
>> variables
> back into a data.frame context.
>
> Everyone's help has been really appreciated! I've learned a lot
> today that
> will hopefully move me slowly from using for loops to more efficient
> functions. I unfortunately am still learning those and have some
> knowledge
> about how to use loops compared to almost no knowledge of the more
> powerful
> functions like sapply, lapply, etc. (I'm waiting on MASS4 to be
> returned to
> the library to read it.)
>
>
> Thanks!
>
>
> John Kane-2 wrote:
>>
>> I think that we probably need a sample database of your original
>> data.
>> A few lines of the dataset would probably be enough as long as it was
>> fairly representative of the overall data set. See ?dput for a way
>> of
>> conveniently supply a sample data set.
>>
>> Otherwise off the top of my head, I would think that you could just
>> put
>> all your subsets into a list and use lapply but I'm simply guessing
>> without seeing the data.
>>
>> --- On Mon, 11/9/09, agm. <amurray@...> wrote:
>>
>>> From: agm. <amurray@...>
>>> Subject: Re: [R] Complicated For Loop (to me)
>>> To: r-help@...
>>> Received: Monday, November 9, 2009, 3:18 PM
>>>
>>> I've looked through ?split and run all of the code, but I
>>> am not sure that I
>>> can use it in such a way to make it do what I need.
>>> Another suggestion was
>>> using "lists", but again, I am sure that the process can do
>>> what I need, but
>>> I am not sure it would work with so many observations.
>>>
>>> I might have been too simple in my code. Let me try
>>> to explain it more
>>> clearly:
>>>
>>> I've got a data set of 4500 observations. I have
>>> already subset it into
>>> race/ethnicity (which I did by simple code). Now I
>>> needed to subset each
>>> race/ethnicity again into 9 separate regions. I again
>>> did this by simple
>>> code.
>>>
>>> The problem is now, I need to calculate a percentage for
>>> three different
>>> variables for all 9 regions for each race. I was
>>> trying to do this through
>>> a loop command.
>>>
>>> So a snippet of my code is :
>>>
>>> names <- c("white", "black", "asian", "hispanic")
>>> for(j in 1:length(names)){
>>> for(i in 1:9){
>>> names[j].cd[i].es.wash <- subset(names[j].cd[i],
>>> SLUNCH==1)
>>> es.cd[i].names.w <-
>>> sum(names.cd[i].es.wash$NWEIGHT)/sum(names.cd[i]$NWEIGHT)
>>> }
>>> }
>>>
>>>
>>> Maybe that makes it clearer. If not, I
>>> apologize. Thanks for the help that
>>> I have already received. It is greatly appreciated.
>>>
>>> Tony

David Winsemius, MD
Heritage Laboratories
West Hartford, CT

______________________________________________
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.

Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T19:53:32Z

Yes, reshaping data is straightforward in R. No need to copy/paste in
a spreadsheet.
See ?reshape and/or the melt/cast functions in the reshape package.

-Ista

On Mon, Nov 9, 2009 at 9:20 PM, Sergios (Sergey) Charntikov
<sergioschr@...> wrote:

> Thank you very much. Finally got it to work. However, I had to recode it from:
> columns: subject/treatment/DV (where all my response data was in one
> DV column) to columns: subject/treatment/day1/day2/day3/ (where my
> response data is now in three different columns).
>
> Is there a way to do that without hand recoding (cutting and pasting
> in spreadsheet) by hand? Thank you for your help. Glad it works as
> is.
>
>
> Sincerely,
>
> Sergios Charntikov (Sergey), MA
>
> Behavioral Neuropharmacology Lab
> Department of Psychology
> University of Nebraska-Lincoln
> Lincoln, NE 68588-0308 USA
>
>
>
>
>
> On Mon, Nov 9, 2009 at 7:12 PM, John Fox <jfox@...> wrote:
>> Dear Sergios,
>>
>> Why don't you try what I suggested originally? Adapted to this data set,
>>
>> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>> idata <- data.frame(Day=factor(1:3))
>> summary(Anova(mod, idata=idata, idesign=~Day))
>>
>> Peter Dalgaard also pointed toward an article that describes how to do the
>> same thing with anova().
>>
>> Regards,
>> John
>>
>>> -----Original Message-----
>>> From: r-help-bounces@... [mailto:r-help-bounces@...]
>> On
>>> Behalf Of Sergios (Sergey) Charntikov
>>> Sent: November-09-09 7:13 PM
>>> To: Mike Lawrence
>>> Cc: r-help@...
>>> Subject: Re: [R] Getting Sphericity Tests for Within Subject Repeated
>> Measure
>>> Anova (using "car" package)
>>>
>>> Hi Mike,
>>>
>>> I tried to run my data in SPSS and it works fine without any problems,
>> plug
>>> in my levels, plug in my covariate (since it is all within) and get my
>>> Mauchly Tests.
>>>
>>> I tried to rearrange the data so it looks like this
>>>
>>> subj/treatment/day1/day2/day3
>>>
>>> subject    treatment    day1    day2    day3
>>> 1    1    8    8    8
>>> 1    2    5    7    5
>>> 2    1    7    4    4
>>> 2    2    4    5    7
>>> 3    1    8    6    4
>>> 3    2    5    2    4
>>> 4    1    2    9    4
>>> 4    2    1    9    1
>>> 5    1    4    8    1
>>> 5    2    7    8    2
>>> 6    1    4    7    2
>>> 6    2    4    5    2
>>>
>>>
>>> When I try mlmfit <- lm(Dataset~1), I get "invalid type (list) for
>> variable
>>> 'Dataset"
>>>
>>> When I try
>>>
>>> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>>>
>>> idata<- data.frame(factor(rep(c(Dataset$day1, Dataset$day2,
>> Dataset$day3))),
>>> ordered(Dataset$Treatment))
>>>
>>> Anova(mod, idata=idata, idesign=~Dataset$Treatment)
>>>
>>> I get: Terms in the intra-subject model matrix are not orthogonal.
>>>
>>> When I try is.matrix(Dataset) - I get no.
>>>
>>> My original mock Dataset (attached in txt) is below. Maybe I am not
>> coding
>>> it right? I would hate to recode all my data for SPSS, since at the end I
>>> would need to show that Sphericity was not violated.
>>>
>>> Subj Trtmt Sessn Response
>>>
>>> 1 N 1 5
>>>
>>> 1 D 1 6
>>>
>>> 1 N 2 4
>>>
>>> 1 D 2 7
>>>
>>> 2 N 1 8
>>>
>>> 2 D 1 9
>>>
>>> 2 N 2 2
>>>
>>> 2 D 2 1
>>>
>>> 3 N 1 4
>>>
>>> 3 D 1 5
>>>
>>> 3 N 2 6
>>>
>>> 3 D 2 2
>>>
>>> 4 N 1 5
>>>
>>> 4 D 1 6
>>>
>>> 4 N 2 4
>>>
>>> 4 D 2 7
>>>
>>> 5 N 1 8
>>>
>>> 5 D 1 9
>>>
>>> 5 N 2 2
>>>
>>> 5 D 2 1
>>>
>>> 6 N 1 4
>>>
>>> 6 D 1 5
>>>
>>> 6 N 2 6
>>>
>>> 6 D 2 2
>>>
>>>
>>>
>>>
>>> Sincerely,
>>>
>>> Sergios Charntikov (Sergey), MA
>>>
>>> Behavioral Neuropharmacology Lab
>>> Department of Psychology
>>> University of Nebraska-Lincoln
>>> Lincoln, NE 68588-0308 USA
>>>
>>>
>>>
>>> On Mon, Nov 9, 2009 at 5:29 PM, Mike Lawrence <Mike.Lawrence@...>
>> wrote:
>>> >
>>> > No luck as in...? What error did you encounter?
>>> >
>>> > In your example data set, you only have 2 levels of each within-Ss
>>> > factor, in which case you shouldn't expect to obtain tests of
>>> > sphericity; as far as I understand it, sphericity necessarily holds
>>> > when for repeated measures with only 2 levels and tests are really
>>> > only possible for repeated measures with 3 or more levels.
>>> >
>>> > I think it's analogous to how you don't need to test homogeneity of
>>> > variance when performing a paired t-test; the test ends up
>>> > representing the pairs as single distribution of difference scores
>>> > with a single variance.
>>> >
>>> > Mike
>>> >
>>> > On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
>>> > <sergioschr@...> wrote:
>>> > > Tried EZanova, no luck with my particular dataset.
>>> > >
>>> > >
>>> > > Sincerely,
>>> > >
>>> > > Sergios Charntikov (Sergey), MA
>>> > >
>>> > > Behavioral Neuropharmacology Lab
>>> > > Department of Psychology
>>> > > University of Nebraska-Lincoln
>>> > > Lincoln, NE 68588-0308 USA
>>> > >
>>> > >
>>> > >
>>> > >
>>> > > On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...>
>>> wrote:
>>> > >>
>>> > >> Have you tried ezANOVA from the ez pacakge? It attempts to provide
>>> > >> a simple user interface to car's ANOVA (and when that fails, aov).
>>> > >>
>>> > >> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
>>> > >> <sergioschr@...> wrote:
>>> > >> > Hello everyone,
>>> > >> >
>>> > >> > I am trying to do within subjects repeated measures anova
>>> > >> > followed by the test of sphericity (sample dataset below).
>>> > >> > I am able to get either mixed model or linear model anova and
>>> > >> > TukeyHSD, but have no luck with Repeated-Measures Assuming
>>> > >> > Sphericity or Separate Sphericity Tests.
>>> > >> > I am trying to follow example from "car" package, but it seems
>>> > >> > that I am not getting something right.
>>> > >> >
>>> > >> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
>>> > >> >
>>> > >> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
>>> > >> >
>>> > >> >> summary(LinearModel.1)
>>> > >> >
>>> > >> > All, good so far, but I have problem understanding "idata=" and
>>> > >> > "idesign="
>>> > >> > functions pertaining to my example. Session is my repeated
>>> > >> > measure (Sessn 1 and Sessn 2 = two sessions, in reality I have
>>> > >> > more) and it is already stacked. Any help or guidance on this
>>> > >> > matter.
>>> > >> >
>>> > >> > Thank you, my mock dataset is below. Each subject has two levels
>>> > >> > of treatment throughout four calendar days which are recoded to
>>> > >> > Session 1 and Session 2 in order to compare treatments by the
>>> > >> > first and subsequent days of exposure (Treatment x Session; my DV
>>> > >> > is Response; Session is repeated).
>>> > >> >
>>> > >> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7
>>> > >> > 2 N 1
>>> > >> > 8
>>> > >> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4
>>> > >> > N 1 5
>>> > >> > 4 D
>>> > >> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1
>>> > >> > 4 6 D
>>> > >> > 1 5
>>> > >> > 6 N 2 6 6 D 2 2
>>> > >> >
>>> > >> > Sincerely,
>>> > >> >
>>> > >> > Sergios Charntikov (Sergey), MA
>>> > >> >
>>> > >> > Behavioral Neuropharmacology Lab
>>> > >> > Department of Psychology
>>> > >> > University of Nebraska-Lincoln
>>> > >> > Lincoln, NE 68588-0308 USA
>>> > >> >
>>> > >> > sergioschr-at-gmail-dot-com
>>> > >> >
>>> > >> > [[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.
>>> > >> >
>>> > >> >
>>> > >>
>>> > >>
>>> > >>
>>> > >> --
>>> > >> 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. ~
>>> > >
>>> > >
>>> >
>>> >
>>> >
>>> > --
>>> > 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. ~
>>
>>
>>
>
> ______________________________________________
> 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

______________________________________________
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.

Re: Complicated For Loop (to me)

2009-11-09T18:51:54Z

Sorry, I've been trying to work around this and just got back to check my email.

dput wasn't working too well for me because the data set also has 450 variables and I needed more time to figure out how to properly show you all what you needed to know.

But to show you the idea, a very simple data set would be:

NWEIGHT ETHNIC RACE SLUNCH DIVISION .......
1234 0 1 1 1
2345 1 1 0 5
3243 0 3 1 3
. . . . .
. . . . .
. . . . .
. . . . .

So basically, I already have the data subset by division and race. (I did that the inefficient way by coding it by hand)

But now I need to calculate the percentage of each division (by race) that participates in SLUNCH (a 0 1 variable)

So I am trying to avoid writing out code such as:

w.cd1.s <- sum(ifelse(white.cd1$SLUNCH==1, white.cd1$NWEIGHT, 0))/sum(white.cd1$NWEIGHT)
w.cd2.s <- sum(ifelse(white.cd2$SLUNCH==1, white.cd2$NWEIGHT, 0))/sum(white.cd2$NWEIGHT)
.... for all the variables.

One other method that I tried, which gets me the "names" i need, but doesn't put them into a dataframe (which I am currently trying to fix) is by using this code:

names <- c("white","black","hispanic","asian")
regions <- c("cd1","cd2","cd3","cd4","cd5","cd6","cd7","cd8","cd9")
type <- c("l", "p", "r")
name.region <- c()
for (j in 1:length(names)){
for(i in 1:length(regions)){
for(k in 1:length(type)){
name.holder <- paste(names[j],".",paste(regions[i],".", type[k], sep=""), sep="")
name.region <- c(name.region, name.holder)
}
}
}

(The "l", "p", "r" represent other variables that I am trying to do the same thing as SLUNCH)

From here I've been trouble-shooting how to switch these named variables back into a data.frame context.

Everyone's help has been really appreciated! I've learned a lot today that will hopefully move me slowly from using for loops to more efficient functions. I unfortunately am still learning those and have some knowledge about how to use loops compared to almost no knowledge of the more powerful functions like sapply, lapply, etc. (I'm waiting on MASS4 to be returned to the library to read it.)

Thanks!

John Kane-2 wrote:

I think that we probably need a sample database of your original data.
A few lines of the dataset would probably be enough as long as it was fairly representative of the overall data set. See ?dput for a way of conveniently supply a sample data set.

Otherwise off the top of my head, I would think that you could just put all your subsets into a list and use lapply but I'm simply guessing without seeing the data.

--- On Mon, 11/9/09, agm. <amurray@vt.edu> wrote:

> From: agm. <amurray@vt.edu>
> Subject: Re: [R] Complicated For Loop (to me)
> To: r-help@r-project.org
> Received: Monday, November 9, 2009, 3:18 PM
>
> I've looked through ?split and run all of the code, but I
> am not sure that I
> can use it in such a way to make it do what I need.
> Another suggestion was
> using "lists", but again, I am sure that the process can do
> what I need, but
> I am not sure it would work with so many observations.
>
> I might have been too simple in my code. Let me try
> to explain it more
> clearly:
>
> I've got a data set of 4500 observations. I have
> already subset it into
> race/ethnicity (which I did by simple code). Now I
> needed to subset each
> race/ethnicity again into 9 separate regions. I again
> did this by simple
> code.
>
> The problem is now, I need to calculate a percentage for
> three different
> variables for all 9 regions for each race. I was
> trying to do this through
> a loop command.
>
> So a snippet of my code is :
>
> names <- c("white", "black", "asian", "hispanic")
> for(j in 1:length(names)){
> for(i in 1:9){
> names[j].cd[i].es.wash <- subset(names[j].cd[i],
> SLUNCH==1)
> es.cd[i].names.w <-
> sum(names.cd[i].es.wash$NWEIGHT)/sum(names.cd[i]$NWEIGHT)
> }
> }
>
>
> Maybe that makes it clearer. If not, I
> apologize. Thanks for the help that
> I have already received. It is greatly appreciated.
>
> Tony
>
> --
> View this message in context: http://old.nabble.com/Complicated-For-Loop-%28to-me%29-tp26269479p26272994.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.
>

__________________________________________________________________
Make your browsing faster, safer, and easier with the new Internet Explorer® 8. Optimized for Yahoo! Get i
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and provide commented, minimal, self-contained, reproducible code.

Re: ordered factor and unordered factor

2009-11-09T18:39:27Z

Why the contrast matrices are different for order and unordered factored?

On Mon, Nov 9, 2009 at 1:12 PM, Greg Snow <Greg.Snow@...> wrote:

> Mostly it is a conceptual difference. An unordered factor is one where there is no inherent order to the levels, examples:
>
> Color of car
> Race
> Nationality
> Sex
> State/Country of birth
> Etc.
>
> In the above, the order of the levels could be changed without it really changing the meaning (think of the order of bars in a bar chart). We may want to print/plot in some specific order such as alphabetic for easy lookup or based on the summary values of another vector for nice looking plots, but there is no overriding reason why we would order color as blue/green/red vs. green/red/blue, etc.
>
> Ordered factors have some natural order, for example maybe you are studying a drug and have doses labeled as Low, Medium, and High. It makes the most sense to print and plot in that order rather than alphabetically (High, Low, Medium). Any continuous variable that has been cut into categories (best not to do this, but if done) has a natural order. Survey questions where you response can range from strongly disagree to strongly agree are usually ordered (but there may be disagreement on what the correct ordering is).
>
> In R the most apparent effects of using ordered vs. factor is in how they print out and how some modeling functions default to handling them (the default contrasts for ordered factors is different, rpart treats ordered factors differently).
>
> Hope this helps,
>
> --
> Gregory (Greg) L. Snow Ph.D.
> Statistical Data Center
> Intermountain Healthcare
> greg.snow@...
> 801.408.8111
>
>
>> -----Original Message-----
>> From: r-help-bounces@... [mailto:r-help-bounces@r-
>> project.org] On Behalf Of Peng Yu
>> Sent: Sunday, November 08, 2009 4:28 PM
>> To: r-help@...
>> Subject: [R] ordered factor and unordered factor
>>
>> I don't understand under what situation ordered factor rather than
>> unordered factor should be used. Could somebody give me some examples?
>> What are the implications of order vs. unordered factors? Could
>> somebody recommend a textbook to me?
>>
>> ______________________________________________
>> 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.
>

Re: What is the equivalent of column.prods() from S in R?

2009-11-09T18:35:54Z

On Nov 9, 2009, at 8:43 PM, Peng Yu wrote:

> Chambers' book Statistical Models in S mentioned 'column.prods()'. But
> I don't find it in R. I'm wondering if there is an equivalent in R?

??rowProds
??colProds

(They are in both fUtilities and timeSeries.)

--

David Winsemius, MD
Heritage Laboratories
West Hartford, CT

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

When is a function call an expression? (multicore package)

2009-11-09T18:28:23Z

I'm trying to understand how to use the multicore package. In
particular, I'm trying to work out what is covered where it says
this in the help file for the parallel function:

expr: expression to evaluate (do _not_ use any on-screen devices or
GUI elements in this code)

Can a function that produces an EPS file be considered an "expression"
in this case? I've tried to run two similar functions in parallel but
I'm obviously not using the term "expression" the way it's intended.

I've made a trivial dummy function:

dummy <- function() {
require(multicore)
aa <- function() {
postscript(file = "AA.eps", height = 220/25.4,
width = 220/25.4, paper = "special", pointsize = 15)
plot(1:10)
dev.off()
}
bb <- function() {
postscript(file = "BB.eps", height = 220/25.4,
width = 220/25.4, paper = "special", pointsize = 15)
plot(11:20)
dev.off()
}
aaa <- mcparallel(aa())
bbb <- mcparallel(bb())
}

The AA.eps file is created, but nothing else. If I try a more
elaborate function, I get even less.

I tried using collect() after the two calls to mcparallel which gave
this puzzling message:

Error in !wait : invalid argument type
> Xlib: unexpected async reply (sequence 0x80)!

Am I trying to do something multicore wasn't designed to do, or am I
just going about it the wrong way?

******************************************
> sessionInfo()
R version 2.10.0 (2009-10-26)
i686-pc-linux-gnu

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] splines grDevices utils stats graphics methods base

other attached packages:
[1] multicore_0.1-3 gbm_1.6-3 survival_2.35-7 lattice_0.17-26

loaded via a namespace (and not attached):
[1] grid_2.10.0 plyr_0.1.9 tools_2.10.0
>
*******************************************************

TIA

Patrick

www.slingshot.co.nz/mobile

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

Inconsistent confidence intervals for lmer sigma from mcmcsamp

2009-11-09T18:26:39Z

I have been trying to estimate confidence interval for the residual sigma in a two-way nested ANOVA, using the lmer and mcmcsamp functions. However, the MCMC confidence interval for sigma not only does not include the point estimate for sigma estimated by lmer but is far away.

My model is an intercept only model with random effects for study and animal (animal nested within study). Here is the output from lmer and mcmcsamp. Any idea why this inconsistency between the lmer estimate of sigma and the corresponding confidence interval from mcmcsamp (is there an alternative way to calculate the confidence interval)?

Many thanks.

Moni

> summary(test.mod)
Linear mixed model fit by REML
Formula: log.count ~ 1 + (1 | study_id/animal_id)
Data: flow
Subset: (cell_type == "B cell") & (cd_group_new == "CD20+")
AIC BIC logLik deviance REMLdev
686 705.7 -339 675.2 678
Random effects:
Groups Name Variance Std.Dev.
animal_id:study_id (Intercept) 0.232873 0.48257
study_id (Intercept) 0.097440 0.31215
Residual 0.052352 0.22880
Number of obs: 1009, groups: animal_id:study_id, 295; study_id, 12

Fixed effects:
Estimate Std. Error t value
(Intercept) 0.1400 0.0969 1.445

> HPDinterval(mcmcsamp(test.mod,20000))
$fixef
lower upper
(Intercept) -0.02019847 0.2775921
attr(,"Probability")
[1] 0.95

$ST
lower upper
[1,] 0.5803595 0.7124204
[2,] 0.4734074 0.9662538
attr(,"Probability")
[1] 0.95

$sigma
lower upper
[1,] 0.3348477 0.3748551
attr(,"Probability")
[1] 0.95

Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T18:20:33Z

Thank you very much. Finally got it to work. However, I had to recode it from:
columns: subject/treatment/DV (where all my response data was in one
DV column) to columns: subject/treatment/day1/day2/day3/ (where my
response data is now in three different columns).

Is there a way to do that without hand recoding (cutting and pasting
in spreadsheet) by hand? Thank you for your help. Glad it works as
is.

Sincerely,

Sergios Charntikov (Sergey), MA

Behavioral Neuropharmacology Lab
Department of Psychology
University of Nebraska-Lincoln
Lincoln, NE 68588-0308 USA

On Mon, Nov 9, 2009 at 7:12 PM, John Fox <jfox@...> wrote:

> Dear Sergios,
>
> Why don't you try what I suggested originally? Adapted to this data set,
>
> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
> idata <- data.frame(Day=factor(1:3))
> summary(Anova(mod, idata=idata, idesign=~Day))
>
> Peter Dalgaard also pointed toward an article that describes how to do the
> same thing with anova().
>
> Regards,
> John
>
>> -----Original Message-----
>> From: r-help-bounces@... [mailto:r-help-bounces@...]
> On
>> Behalf Of Sergios (Sergey) Charntikov
>> Sent: November-09-09 7:13 PM
>> To: Mike Lawrence
>> Cc: r-help@...
>> Subject: Re: [R] Getting Sphericity Tests for Within Subject Repeated
> Measure
>> Anova (using "car" package)
>>
>> Hi Mike,
>>
>> I tried to run my data in SPSS and it works fine without any problems,
> plug
>> in my levels, plug in my covariate (since it is all within) and get my
>> Mauchly Tests.
>>
>> I tried to rearrange the data so it looks like this
>>
>> subj/treatment/day1/day2/day3
>>
>> subject    treatment    day1    day2    day3
>> 1    1    8    8    8
>> 1    2    5    7    5
>> 2    1    7    4    4
>> 2    2    4    5    7
>> 3    1    8    6    4
>> 3    2    5    2    4
>> 4    1    2    9    4
>> 4    2    1    9    1
>> 5    1    4    8    1
>> 5    2    7    8    2
>> 6    1    4    7    2
>> 6    2    4    5    2
>>
>>
>> When I try mlmfit <- lm(Dataset~1), I get "invalid type (list) for
> variable
>> 'Dataset"
>>
>> When I try
>>
>> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>>
>> idata<- data.frame(factor(rep(c(Dataset$day1, Dataset$day2,
> Dataset$day3))),
>> ordered(Dataset$Treatment))
>>
>> Anova(mod, idata=idata, idesign=~Dataset$Treatment)
>>
>> I get: Terms in the intra-subject model matrix are not orthogonal.
>>
>> When I try is.matrix(Dataset) - I get no.
>>
>> My original mock Dataset (attached in txt) is below. Maybe I am not
> coding
>> it right? I would hate to recode all my data for SPSS, since at the end I
>> would need to show that Sphericity was not violated.
>>
>> Subj Trtmt Sessn Response
>>
>> 1 N 1 5
>>
>> 1 D 1 6
>>
>> 1 N 2 4
>>
>> 1 D 2 7
>>
>> 2 N 1 8
>>
>> 2 D 1 9
>>
>> 2 N 2 2
>>
>> 2 D 2 1
>>
>> 3 N 1 4
>>
>> 3 D 1 5
>>
>> 3 N 2 6
>>
>> 3 D 2 2
>>
>> 4 N 1 5
>>
>> 4 D 1 6
>>
>> 4 N 2 4
>>
>> 4 D 2 7
>>
>> 5 N 1 8
>>
>> 5 D 1 9
>>
>> 5 N 2 2
>>
>> 5 D 2 1
>>
>> 6 N 1 4
>>
>> 6 D 1 5
>>
>> 6 N 2 6
>>
>> 6 D 2 2
>>
>>
>>
>>
>> Sincerely,
>>
>> Sergios Charntikov (Sergey), MA
>>
>> Behavioral Neuropharmacology Lab
>> Department of Psychology
>> University of Nebraska-Lincoln
>> Lincoln, NE 68588-0308 USA
>>
>>
>>
>> On Mon, Nov 9, 2009 at 5:29 PM, Mike Lawrence <Mike.Lawrence@...>
> wrote:
>> >
>> > No luck as in...? What error did you encounter?
>> >
>> > In your example data set, you only have 2 levels of each within-Ss
>> > factor, in which case you shouldn't expect to obtain tests of
>> > sphericity; as far as I understand it, sphericity necessarily holds
>> > when for repeated measures with only 2 levels and tests are really
>> > only possible for repeated measures with 3 or more levels.
>> >
>> > I think it's analogous to how you don't need to test homogeneity of
>> > variance when performing a paired t-test; the test ends up
>> > representing the pairs as single distribution of difference scores
>> > with a single variance.
>> >
>> > Mike
>> >
>> > On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
>> > <sergioschr@...> wrote:
>> > > Tried EZanova, no luck with my particular dataset.
>> > >
>> > >
>> > > Sincerely,
>> > >
>> > > Sergios Charntikov (Sergey), MA
>> > >
>> > > Behavioral Neuropharmacology Lab
>> > > Department of Psychology
>> > > University of Nebraska-Lincoln
>> > > Lincoln, NE 68588-0308 USA
>> > >
>> > >
>> > >
>> > >
>> > > On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...>
>> wrote:
>> > >>
>> > >> Have you tried ezANOVA from the ez pacakge? It attempts to provide
>> > >> a simple user interface to car's ANOVA (and when that fails, aov).
>> > >>
>> > >> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
>> > >> <sergioschr@...> wrote:
>> > >> > Hello everyone,
>> > >> >
>> > >> > I am trying to do within subjects repeated measures anova
>> > >> > followed by the test of sphericity (sample dataset below).
>> > >> > I am able to get either mixed model or linear model anova and
>> > >> > TukeyHSD, but have no luck with Repeated-Measures Assuming
>> > >> > Sphericity or Separate Sphericity Tests.
>> > >> > I am trying to follow example from "car" package, but it seems
>> > >> > that I am not getting something right.
>> > >> >
>> > >> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
>> > >> >
>> > >> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
>> > >> >
>> > >> >> summary(LinearModel.1)
>> > >> >
>> > >> > All, good so far, but I have problem understanding "idata=" and
>> > >> > "idesign="
>> > >> > functions pertaining to my example. Session is my repeated
>> > >> > measure (Sessn 1 and Sessn 2 = two sessions, in reality I have
>> > >> > more) and it is already stacked. Any help or guidance on this
>> > >> > matter.
>> > >> >
>> > >> > Thank you, my mock dataset is below. Each subject has two levels
>> > >> > of treatment throughout four calendar days which are recoded to
>> > >> > Session 1 and Session 2 in order to compare treatments by the
>> > >> > first and subsequent days of exposure (Treatment x Session; my DV
>> > >> > is Response; Session is repeated).
>> > >> >
>> > >> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7
>> > >> > 2 N 1
>> > >> > 8
>> > >> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4
>> > >> > N 1 5
>> > >> > 4 D
>> > >> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1
>> > >> > 4 6 D
>> > >> > 1 5
>> > >> > 6 N 2 6 6 D 2 2
>> > >> >
>> > >> > Sincerely,
>> > >> >
>> > >> > Sergios Charntikov (Sergey), MA
>> > >> >
>> > >> > Behavioral Neuropharmacology Lab
>> > >> > Department of Psychology
>> > >> > University of Nebraska-Lincoln
>> > >> > Lincoln, NE 68588-0308 USA
>> > >> >
>> > >> > sergioschr-at-gmail-dot-com
>> > >> >
>> > >> > [[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.
>> > >> >
>> > >> >
>> > >>
>> > >>
>> > >>
>> > >> --
>> > >> 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. ~
>> > >
>> > >
>> >
>> >
>> >
>> > --
>> > 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. ~
>
>
>

Re: Java and R

2009-11-09T17:49:20Z

Grzes wrote:
> Hello,
> I'm looking for any manual about using Java and R for begginers. Do you know
> any?
>
Take a look at Rserve, rJava, etc..

-cj

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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.

What is the equivalent of column.prods() from S in R?

2009-11-09T17:43:18Z

Chambers' book Statistical Models in S mentioned 'column.prods()'. But
I don't find it in R. I'm wondering if there is an equivalent in R?

______________________________________________
R-help@... mailing list
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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.

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

2009-11-09T17:31:53Z

On Sun, Nov 8, 2009 at 7:32 PM, John Fox <jfox@...> wrote:
> Dear Peng,
>
> I'm tempted to try to get an entry in the fortunes package but will instead
> try to answer your questions directly:

I can not install 'fortunes'. What are the fortunes packages about?
> install.packages("fortunes", repos="http://R-Forge.R-project.org")
Warning: unable to access index for repository
http://R-Forge.R-project.org/bin/macosx/leopard/contrib/2.9
Warning message:
In getDependencies(pkgs, dependencies, available, lib) :
package ‘fortunes’ is not available

>> 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.

What is the mathematical definition of polynomial contrasts? Why
polynomial contrasts are the default contrasts for ordered factors?

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

Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T17:12:29Z

Dear Sergios,

Why don't you try what I suggested originally? Adapted to this data set,

mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
idata <- data.frame(Day=factor(1:3))
summary(Anova(mod, idata=idata, idesign=~Day))

Peter Dalgaard also pointed toward an article that describes how to do the
same thing with anova().

Regards,
John

> -----Original Message-----
> From: r-help-bounces@... [mailto:r-help-bounces@...]
On
> Behalf Of Sergios (Sergey) Charntikov
> Sent: November-09-09 7:13 PM
> To: Mike Lawrence
> Cc: r-help@...
> Subject: Re: [R] Getting Sphericity Tests for Within Subject Repeated
Measure
> Anova (using "car" package)
>
> Hi Mike,
>
> I tried to run my data in SPSS and it works fine without any problems,
plug

> in my levels, plug in my covariate (since it is all within) and get my
> Mauchly Tests.
>
> I tried to rearrange the data so it looks like this
>
> subj/treatment/day1/day2/day3
>
> subject    treatment    day1    day2    day3
> 1    1    8    8    8
> 1    2    5    7    5
> 2    1    7    4    4
> 2    2    4    5    7
> 3    1    8    6    4
> 3    2    5    2    4
> 4    1    2    9    4
> 4    2    1    9    1
> 5    1    4    8    1
> 5    2    7    8    2
> 6    1    4    7    2
> 6    2    4    5    2
>
>
> When I try mlmfit <- lm(Dataset~1), I get "invalid type (list) for

variable
> 'Dataset"
>
> When I try
>
> mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)
>
> idata<- data.frame(factor(rep(c(Dataset$day1, Dataset$day2,
Dataset$day3))),
> ordered(Dataset$Treatment))
>
> Anova(mod, idata=idata, idesign=~Dataset$Treatment)
>
> I get: Terms in the intra-subject model matrix are not orthogonal.
>
> When I try is.matrix(Dataset) - I get no.
>
> My original mock Dataset (attached in txt) is below. Maybe I am not
coding

> it right? I would hate to recode all my data for SPSS, since at the end I
> would need to show that Sphericity was not violated.
>
> Subj Trtmt Sessn Response
>
> 1 N 1 5
>
> 1 D 1 6
>
> 1 N 2 4
>
> 1 D 2 7
>
> 2 N 1 8
>
> 2 D 1 9
>
> 2 N 2 2
>
> 2 D 2 1
>
> 3 N 1 4
>
> 3 D 1 5
>
> 3 N 2 6
>
> 3 D 2 2
>
> 4 N 1 5
>
> 4 D 1 6
>
> 4 N 2 4
>
> 4 D 2 7
>
> 5 N 1 8
>
> 5 D 1 9
>
> 5 N 2 2
>
> 5 D 2 1
>
> 6 N 1 4
>
> 6 D 1 5
>
> 6 N 2 6
>
> 6 D 2 2
>
>
>
>
> Sincerely,
>
> Sergios Charntikov (Sergey), MA
>
> Behavioral Neuropharmacology Lab
> Department of Psychology
> University of Nebraska-Lincoln
> Lincoln, NE 68588-0308 USA
>
>
>
> On Mon, Nov 9, 2009 at 5:29 PM, Mike Lawrence <Mike.Lawrence@...>

wrote:

> >
> > No luck as in...? What error did you encounter?
> >
> > In your example data set, you only have 2 levels of each within-Ss
> > factor, in which case you shouldn't expect to obtain tests of
> > sphericity; as far as I understand it, sphericity necessarily holds
> > when for repeated measures with only 2 levels and tests are really
> > only possible for repeated measures with 3 or more levels.
> >
> > I think it's analogous to how you don't need to test homogeneity of
> > variance when performing a paired t-test; the test ends up
> > representing the pairs as single distribution of difference scores
> > with a single variance.
> >
> > Mike
> >
> > On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
> > <sergioschr@...> wrote:
> > > Tried EZanova, no luck with my particular dataset.
> > >
> > >
> > > Sincerely,
> > >
> > > Sergios Charntikov (Sergey), MA
> > >
> > > Behavioral Neuropharmacology Lab
> > > Department of Psychology
> > > University of Nebraska-Lincoln
> > > Lincoln, NE 68588-0308 USA
> > >
> > >
> > >
> > >
> > > On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...>
> wrote:
> > >>
> > >> Have you tried ezANOVA from the ez pacakge? It attempts to provide
> > >> a simple user interface to car's ANOVA (and when that fails, aov).
> > >>
> > >> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
> > >> <sergioschr@...> wrote:
> > >> > Hello everyone,
> > >> >
> > >> > I am trying to do within subjects repeated measures anova
> > >> > followed by the test of sphericity (sample dataset below).
> > >> > I am able to get either mixed model or linear model anova and
> > >> > TukeyHSD, but have no luck with Repeated-Measures Assuming
> > >> > Sphericity or Separate Sphericity Tests.
> > >> > I am trying to follow example from "car" package, but it seems
> > >> > that I am not getting something right.
> > >> >
> > >> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
> > >> >
> > >> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
> > >> >
> > >> >> summary(LinearModel.1)
> > >> >
> > >> > All, good so far, but I have problem understanding "idata=" and
> > >> > "idesign="
> > >> > functions pertaining to my example. Session is my repeated
> > >> > measure (Sessn 1 and Sessn 2 = two sessions, in reality I have
> > >> > more) and it is already stacked. Any help or guidance on this
> > >> > matter.
> > >> >
> > >> > Thank you, my mock dataset is below. Each subject has two levels
> > >> > of treatment throughout four calendar days which are recoded to
> > >> > Session 1 and Session 2 in order to compare treatments by the
> > >> > first and subsequent days of exposure (Treatment x Session; my DV
> > >> > is Response; Session is repeated).
> > >> >
> > >> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7
> > >> > 2 N 1
> > >> > 8
> > >> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4
> > >> > N 1 5
> > >> > 4 D
> > >> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1
> > >> > 4 6 D
> > >> > 1 5
> > >> > 6 N 2 6 6 D 2 2
> > >> >
> > >> > Sincerely,
> > >> >
> > >> > Sergios Charntikov (Sergey), MA
> > >> >
> > >> > Behavioral Neuropharmacology Lab
> > >> > Department of Psychology
> > >> > University of Nebraska-Lincoln
> > >> > Lincoln, NE 68588-0308 USA
> > >> >
> > >> > sergioschr-at-gmail-dot-com
> > >> >
> > >> > [[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.
> > >> >
> > >> >
> > >>
> > >>
> > >>
> > >> --
> > >> 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. ~
> > >
> > >
> >
> >
> >
> > --
> > 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. ~

Re: Automating Plot Commands using a Loop

2009-11-09T17:05:26Z

try this:

Data <- read.table(textConnection(" Alpha Beta Gamma Delta
A .1 .2 .3 .4
B .2 .3 .4 .5
C .8 .9 .43 .13
D .13 .34 .34 .3"), header=TRUE)
closeAllConnections()

par(mfrow=c(2,2))
for (i in colnames(Data)){
plot(Data[[i]],type="o",axes=F,xlab='', ylab='', ylim=c(0,1), main=i)
axis(1,at=1:4,lab=c("A","B","C","D"))
axis(2,at=seq(0, 1, .25)) # changed your axis to work
}

On Mon, Nov 9, 2009 at 3:38 PM, Koraelus <koraelus@...> wrote:

>
> I have three matrices with the same row and column names, but different data.
>
> e.g.
>
> Data
> Alpha Beta Gamma Delta
> A .1 .2 .3 .4
> B .2 .3 .4 .5
> C .8 .9 .43 .13
> D .13 .34 .34 .3
>
>
> For each column, I would like to create a separate plot on a single window.
> I currently am using the cmd-line:
>
> windows()
> par(mfrow=c(2,2))
> plot(Data$Alpha,type="o",axes=F,ann=F,ylim=c(0,1))
> axis(1,at=1:4,lab=c("A","B","C","D")
> axis(2,at=.25*0:1)
> plot(Data$Beta,type="o",axes=F,ann=F,ylim=c(0,1))
> axis(1,at=1:4,lab=c("A","B","C","D")
> axis(2,at=.25*0:1)
> etc.
>
> I would like to automate this as much as possible. I tried to write a
> function, but clearly it would involve some sort of loop... I don't know how
> to do anything like that.
>
> Thanks,
> koraelus
> --
> View this message in context: http://old.nabble.com/Automating-Plot-Commands-using-a-Loop-tp26273268p26273268.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.
>

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

What is the problem that you are trying to solve?

______________________________________________
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.

help, GA for varialbe selection. ?!

2009-11-09T16:54:10Z

Dear all,
I am learning the subselect package in R, now I want to use GA to select some potent variable, but some questions are puzzled.
what i want to resolve is that I have one column dependent y and 219 columns independent x. A total 72 observations is contained in the dataset. I want to select some variables to fit to y. GA is good method to do it as many paper said. but they often test the model by some criteria(i.e. LOO r2 (q2), standard deviaton(sd), etc.). can the "subselect package" get these index(LOO r2,sd) ?
The example in the subselcet manual:
data(swiss)
genetic(cor(swiss),3,4,popsize=10,nger=5,criterion="Rv") is just about matrix X, refer to my data is 72 X 219 matrix. how can I correlate with dependent y(72 X 1 matrix)? I think whether I can use GA to select some variable only from X matrix(independent), and then use the best subset to fit y(dependent) using another algorithm(not containing the subselect package),and then calculate LOO r2 and sd etc. ?
The last questions is that, these 219 indepents have correlation each other, need they pre-deal with befor applying GA selection? e.g.remove one of any two indices with a correlation greater than 0.90.
I send my data to the accessory. it is a csv file. the first column is dependent (y),and the remaining columns are independent. I want to model a equation with good statistic result(high LOO r2, low sd). HOw Can do.
Thank you!

Best wishes
Kevin mk1.df.csv

Re: Complicated For Loop (to me)

2009-11-09T16:50:52Z

Since you did not follow the posting and provide data, here is a way
that you can split by race & region and perform operations on each
subset of the data frame:

> # test data
> n <- 4500
> x <- data.frame(race=sample(c('a','b','c'), n, TRUE),
+ region=sample(1:9, n, TRUE), values=runif(n))
> # split into race & region
> x.s <- split(x, list(x$race, x$region), drop=TRUE)
> # average for each subset
> sapply(x.s, function(.df) mean(.df$values))
a.1 b.1 c.1 a.2 b.2 c.2 a.3
b.3 c.3 a.4
0.4832214 0.4792623 0.4846138 0.5098792 0.4997666 0.5212905 0.5068535
0.5140438 0.5580490 0.4849455
b.4 c.4 a.5 b.5 c.5 a.6 b.6
c.6 a.7 b.7
0.4952947 0.4639553 0.4798192 0.5030650 0.4889465 0.4839610 0.5080325
0.5176548 0.4788206 0.5300395
c.7 a.8 b.8 c.8 a.9 b.9 c.9
0.4799252 0.4871341 0.4675535 0.4899783 0.5172642 0.4968789 0.5499326
>

On Mon, Nov 9, 2009 at 3:18 PM, agm. <amurray@...> wrote:

>
> I've looked through ?split and run all of the code, but I am not sure that I
> can use it in such a way to make it do what I need. Another suggestion was
> using "lists", but again, I am sure that the process can do what I need, but
> I am not sure it would work with so many observations.
>
> I might have been too simple in my code. Let me try to explain it more
> clearly:
>
> I've got a data set of 4500 observations. I have already subset it into
> race/ethnicity (which I did by simple code). Now I needed to subset each
> race/ethnicity again into 9 separate regions. I again did this by simple
> code.
>
> The problem is now, I need to calculate a percentage for three different
> variables for all 9 regions for each race. I was trying to do this through
> a loop command.
>
> So a snippet of my code is :
>
> names <- c("white", "black", "asian", "hispanic")
> for(j in 1:length(names)){
> for(i in 1:9){
> names[j].cd[i].es.wash <- subset(names[j].cd[i], SLUNCH==1)
> es.cd[i].names.w <-
> sum(names.cd[i].es.wash$NWEIGHT)/sum(names.cd[i]$NWEIGHT)
> }
> }
>
>
> Maybe that makes it clearer. If not, I apologize. Thanks for the help that
> I have already received. It is greatly appreciated.
>
> Tony
>
> --
> View this message in context: http://old.nabble.com/Complicated-For-Loop-%28to-me%29-tp26269479p26272994.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.
>

Re: aggregate data.frame

2009-11-09T16:23:49Z

Tena koe Alfredo

?merge

HTH ...

Peter Alspach

> -----Original Message-----
> From: r-help-bounces@...
> [mailto:r-help-bounces@...] On Behalf Of Alfredo
> Alessandrini
> Sent: Tuesday, 10 November 2009 1:16 p.m.
> To: r-help@...
> Subject: [R] aggregate data.frame
>
> Hi,
>
> I've two data.frame: ind_comp and dati_area
>
>
>
> > ind_comp
> INDEX indice
> 1 1 0.3081856
> 2 2 0.1368007
> 3 3 0.1290952
> 4 4 0.2905484
> 5 5 0.2686706
> 6 6 0.1122784
> 7 7 0.4493264
> 8 8 0.1932665
> 9 9 0.1982783
> 10 11 0.3724666
>
> > dati_area
> X_COORD Y_COORD DBH AREA ID ind_comp
> 1 0.0000000 0.0000000 70.0 1 1 0
> 2 4.4352788 14.5071431 70.0 1 2 0
> 3 10.6893894 10.3226234 68.0 1 3 0
> 4 5.8856279 0.4115632 75.5 1 4 0
> 5 5.7429111 -8.5142159 78.5 1 5 0
> 6 -6.4300000 11.1370867 60.0 1 6 0
> 7 0.6358819 -12.1333488 82.4 1 7 0
> 8 -7.9629934 -15.6282544 60.0 1 8 0
> 9 -7.9962383 -7.1998453 62.0 1 9 0
> 10 -17.3282497 5.6302896 67.0 1 10 0
> 11 -3.2569414 5.0152500 77.5 1 12 0
>
>
> I need to populate the column "ind_comp" of the data.frame
> "dati_area", with the data "indice" of the data.frame "ind_comp".
>
> The data "INDEX" of the data.frame "ind_comp" must
> correspond to index of the rows "dati_area".
>
> X_COORD Y_COORD DBH AREA ID ind_comp
> 1 0.0000000 0.0000000 70.0 1 1 0.3081856
> 2 4.4352788 14.5071431 70.0 1 2 0.1368007
> 3 10.6893894 10.3226234 68.0 1 3 0.1290952
> 4 5.8856279 0.4115632 75.5 1 4 0.2905484
> 5 5.7429111 -8.5142159 78.5 1 5 0.2686706
> 6 -6.4300000 11.1370867 60.0 1 6 0.1122784
> 7 0.6358819 -12.1333488 82.4 1 7 0.4493264
> 8 -7.9629934 -15.6282544 60.0 1 8 0.1932665
> 9 -7.9962383 -7.1998453 62.0 1 9 0.1982783
> 10 -17.3282497 5.6302896 67.0 1 10 NA
> 11 -3.2569414 5.0152500 77.5 1 12 0.3724666
>
>
> Thanks,
>
> Alfredo
>
> ______________________________________________
> 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.
>

Re: creating multiple plots using a splitting factor

2009-11-09T16:21:41Z

hist(distance_cm, breaks = 10)
is the **output** of a function and is not itself a function.

On the other hand,
function(distance_cm) hist(distance_cm, breaks = 10)
is a function.

On Mon, Nov 9, 2009 at 7:01 PM, Johnston, Danielle
<Danielle.Bilyeu@...> wrote:

> Hello,
>
>
>
> I am new to R. I often collect data at multiple sites and need to
> create separate graphs (such as scatterplots or histograms) of specific
> variables for each site. I have tried to do this by splitting the data
> frame and then using lapply, but it seems that the graphing commands
> cannot be called as functions. Here is a sample of my data, called
> "seeddist2":
>
>
>
> site DaysSinceRelease distance_cm
>
> 10 GVM 1 17.8
>
> 11 GVM 1 17.8
>
> 12 GVM 1 14.0
>
> 13 GVM 1 14.0
>
> 14 GVM 1 14.9
>
> 15 GVM 1 25.4
>
> 16 WRR 1 25.4
>
> 17 WRR 1 35.0
>
> 18 WRR 1 45.0
>
> 19 WRR 1 55.0
>
> 20 WRR 1 60.0
>
>
>
> Here is what I tried to get separate histograms of distance_cm by site:
>
> splitlist<- split(seeddist2, site)
>
> lapply(splitlist, hist(distance_cm, breaks=10))
>
>
>
> I then get an error message saying that "match.fun" didn't find the
> function. Is there another way to produce multiple graphs at once?
>
>
>
> Thank you,
>
>
>
> Danielle B. Johnston, Habitat Researcher
>
> Colorado Division of Wildlife
>
>
>
>
> [[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.
>

aggregate data.frame

2009-11-09T16:16:24Z

Hi,

I've two data.frame: ind_comp and dati_area

> ind_comp
INDEX indice
1 1 0.3081856
2 2 0.1368007
3 3 0.1290952
4 4 0.2905484
5 5 0.2686706
6 6 0.1122784
7 7 0.4493264
8 8 0.1932665
9 9 0.1982783
10 11 0.3724666

> dati_area
X_COORD Y_COORD DBH AREA ID ind_comp
1 0.0000000 0.0000000 70.0 1 1 0
2 4.4352788 14.5071431 70.0 1 2 0
3 10.6893894 10.3226234 68.0 1 3 0
4 5.8856279 0.4115632 75.5 1 4 0
5 5.7429111 -8.5142159 78.5 1 5 0
6 -6.4300000 11.1370867 60.0 1 6 0
7 0.6358819 -12.1333488 82.4 1 7 0
8 -7.9629934 -15.6282544 60.0 1 8 0
9 -7.9962383 -7.1998453 62.0 1 9 0
10 -17.3282497 5.6302896 67.0 1 10 0
11 -3.2569414 5.0152500 77.5 1 12 0

I need to populate the column "ind_comp" of the data.frame
"dati_area", with the data "indice" of the data.frame "ind_comp".

The data "INDEX" of the data.frame "ind_comp" must correspond to
index of the rows "dati_area".

X_COORD Y_COORD DBH AREA ID ind_comp
1 0.0000000 0.0000000 70.0 1 1 0.3081856
2 4.4352788 14.5071431 70.0 1 2 0.1368007
3 10.6893894 10.3226234 68.0 1 3 0.1290952
4 5.8856279 0.4115632 75.5 1 4 0.2905484
5 5.7429111 -8.5142159 78.5 1 5 0.2686706
6 -6.4300000 11.1370867 60.0 1 6 0.1122784
7 0.6358819 -12.1333488 82.4 1 7 0.4493264
8 -7.9629934 -15.6282544 60.0 1 8 0.1932665
9 -7.9962383 -7.1998453 62.0 1 9 0.1982783
10 -17.3282497 5.6302896 67.0 1 10 NA
11 -3.2569414 5.0152500 77.5 1 12 0.3724666

Thanks,

Alfredo

______________________________________________
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.

Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T16:13:18Z

Hi Mike,

I tried to run my data in SPSS and it works fine without any problems,
plug in my levels, plug in my covariate (since it is all within) and
get my Mauchly Tests.

I tried to rearrange the data so it looks like this

subj/treatment/day1/day2/day3

subject    treatment    day1    day2    day3
1    1    8    8    8
1    2    5    7    5
2    1    7    4    4
2    2    4    5    7
3    1    8    6    4
3    2    5    2    4
4    1    2    9    4
4    2    1    9    1
5    1    4    8    1
5    2    7    8    2
6    1    4    7    2
6    2    4    5    2

When I try mlmfit <- lm(Dataset~1), I get "invalid type (list) for
variable 'Dataset"

When I try

mod <- lm(cbind(day1, day2, day3) ~ Treatment, data=Dataset)

idata<- data.frame(factor(rep(c(Dataset$day1, Dataset$day2, Dataset$day3))),
ordered(Dataset$Treatment))

Anova(mod, idata=idata, idesign=~Dataset$Treatment)

I get: Terms in the intra-subject model matrix are not orthogonal.

When I try is.matrix(Dataset) - I get no.

My original mock Dataset (attached in txt) is below. Maybe I am not
coding it right? I would hate to recode all my data for SPSS, since at
the end I would need to show that Sphericity was not violated.

Subj Trtmt Sessn Response

1 N 1 5

1 D 1 6

1 N 2 4

1 D 2 7

2 N 1 8

2 D 1 9

2 N 2 2

2 D 2 1

3 N 1 4

3 D 1 5

3 N 2 6

3 D 2 2

4 N 1 5

4 D 1 6

4 N 2 4

4 D 2 7

5 N 1 8

5 D 1 9

5 N 2 2

5 D 2 1

6 N 1 4

6 D 1 5

6 N 2 6

6 D 2 2

Sincerely,

Sergios Charntikov (Sergey), MA

Behavioral Neuropharmacology Lab
Department of Psychology
University of Nebraska-Lincoln
Lincoln, NE 68588-0308 USA

On Mon, Nov 9, 2009 at 5:29 PM, Mike Lawrence <Mike.Lawrence@...> wrote:

>
> No luck as in...? What error did you encounter?
>
> In your example data set, you only have 2 levels of each within-Ss
> factor, in which case you shouldn't expect to obtain tests of
> sphericity; as far as I understand it, sphericity necessarily holds
> when for repeated measures with only 2 levels and tests are really
> only possible for repeated measures with 3 or more levels.
>
> I think it's analogous to how you don't need to test homogeneity of
> variance when performing a paired t-test; the test ends up
> representing the pairs as single distribution of difference scores
> with a single variance.
>
> Mike
>
> On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
> <sergioschr@...> wrote:
> > Tried EZanova, no luck with my particular dataset.
> >
> >
> > Sincerely,
> >
> > Sergios Charntikov (Sergey), MA
> >
> > Behavioral Neuropharmacology Lab
> > Department of Psychology
> > University of Nebraska-Lincoln
> > Lincoln, NE 68588-0308 USA
> >
> >
> >
> >
> > On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...> wrote:
> >>
> >> Have you tried ezANOVA from the ez pacakge? It attempts to provide a
> >> simple user interface to car's ANOVA (and when that fails, aov).
> >>
> >> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
> >> <sergioschr@...> wrote:
> >> > Hello everyone,
> >> >
> >> > I am trying to do within subjects repeated measures anova followed by
> >> > the
> >> > test of sphericity (sample dataset below).
> >> > I am able to get either mixed model or linear model anova and TukeyHSD,
> >> > but
> >> > have no luck with Repeated-Measures Assuming Sphericity or Separate
> >> > Sphericity Tests.
> >> > I am trying to follow example from "car" package, but it seems that I am
> >> > not
> >> > getting something right.
> >> >
> >> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
> >> >
> >> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
> >> >
> >> >> summary(LinearModel.1)
> >> >
> >> > All, good so far, but I have problem understanding "idata=" and
> >> > "idesign="
> >> > functions pertaining to my example. Session is my repeated measure
> >> > (Sessn 1
> >> > and Sessn 2 = two sessions, in reality I have more) and it is already
> >> > stacked. Any help or guidance on this matter.
> >> >
> >> > Thank you, my mock dataset is below. Each subject has two levels of
> >> > treatment throughout four calendar days which are recoded to Session 1
> >> > and
> >> > Session 2 in order to compare treatments by the first and subsequent
> >> > days of
> >> > exposure (Treatment x Session; my DV is Response; Session is repeated).
> >> >
> >> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7 2 N 1
> >> > 8
> >> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4 N 1 5
> >> > 4 D
> >> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1 4 6 D
> >> > 1 5
> >> > 6 N 2 6 6 D 2 2
> >> >
> >> > Sincerely,
> >> >
> >> > Sergios Charntikov (Sergey), MA
> >> >
> >> > Behavioral Neuropharmacology Lab
> >> > Department of Psychology
> >> > University of Nebraska-Lincoln
> >> > Lincoln, NE 68588-0308 USA
> >> >
> >> > sergioschr-at-gmail-dot-com
> >> >
> >> > [[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.
> >> >
> >> >
> >>
> >>
> >>
> >> --
> >> 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. ~
> >
> >
>
>
>
> --
> 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. ~

Subj Trtmt Sessn Response
1 N 1 5
1 D 1 6
1 N 2 4
1 D 2 7
2 N 1 8
2 D 1 9
2 N 2 2
2 D 2 1
3 N 1 4
3 D 1 5
3 N 2 6
3 D 2 2
4 N 1 5
4 D 1 6
4 N 2 4
4 D 2 7
5 N 1 8
5 D 1 9
5 N 2 2
5 D 2 1
6 N 1 4
6 D 1 5
6 N 2 6
6 D 2 2

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creating multiple plots using a splitting factor

2009-11-09T16:01:06Z

Hello,

I am new to R. I often collect data at multiple sites and need to
create separate graphs (such as scatterplots or histograms) of specific
variables for each site. I have tried to do this by splitting the data
frame and then using lapply, but it seems that the graphing commands
cannot be called as functions. Here is a sample of my data, called
"seeddist2":

site DaysSinceRelease distance_cm

10 GVM 1 17.8

11 GVM 1 17.8

12 GVM 1 14.0

13 GVM 1 14.0

14 GVM 1 14.9

15 GVM 1 25.4

16 WRR 1 25.4

17 WRR 1 35.0

18 WRR 1 45.0

19 WRR 1 55.0

20 WRR 1 60.0

Here is what I tried to get separate histograms of distance_cm by site:

splitlist<- split(seeddist2, site)

lapply(splitlist, hist(distance_cm, breaks=10))

I then get an error message saying that "match.fun" didn't find the
function. Is there another way to produce multiple graphs at once?

Thank you,

Danielle B. Johnston, Habitat Researcher

Colorado Division of Wildlife

[[alternative HTML version deleted]]

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Re: Hand-crafting an .RData file

2009-11-09T15:54:24Z

If you can manage to write out your data in separate binary files, one for each column, then another possibility is using package ff. You can link those binary columns into R by defining an ffdf dataframe: columns are memory mapped and you can access those parts you need - without initially importing them. This is much faster than a csv import and also works for files that are too large to import at once. If all your columns have the same storage.mode (vmode in ff), then another alternative is writing out all your data in one single binary matrix with major row-order (because that can be written row by row from your program) and link the file into R as a single ff_matrix.

Since ffdf in ff is new, I give a mini-tutorial below.
Let me know how that works for you.

Kind regards

Jens Oehlschlägel

library(ff)

# Create example csv
fnam <- "/tmp/example.csv"
write.csv(data.frame(a=1:9, b=1:9+0.1), file=fnam, row.names=FALSE)

# Create example binary files on disk.
# Reading csv into ffdf actually stores
# each column as a binary file on disk.
# Using a pattern outside fftempdir automatically sets finalizer="close"
# and thus makes those binary files permanent.
path <- "/tmp/example_"
x <- read.csv.ffdf(file=fnam, ff_args=list(pattern=path))
close(x)

# Note that a standard ffdf is made-up column by column from simple ff objects.
# More coplex mappings from ff objects into ffdf are possible,
# but let's keep it simple for now.
p <- physical(x)
p

# Now let's just create an ffdf from existing binary files.
# Step one: create an ff object for each binary file (without reading them).

# Note that because we open ff files outside fftempdir,
# the default finalizer is "close", not "delete",
# so the file will not be deleted on finalization
# files are opened for memory mapping, but not read
ffcols <- vector("list", length(p))
for (i in 1:length(p)){
ffcols[[i]] <- ff(filename=filename(p[[i]]), vmode=vmode(p[[i]]))
}
ffcols

# step two: bundle several ff objects into one ffdf data.frame
# (still without reading data)
ffdafr <- ffdf(a=ffcols[[1]], b=ffcols[[2]])

# now reading rows from this will return a standard data.frame
# (and only read the required rows)
ffdafr[1:4,]
ffdafr[5:9,]

# As an alternative create an example binary
# (double) matrix in major row order
y <- as.ff(t(ffdafr[,]), filename="d:/tmp/example_single_matrix.ff")

# Again we can link this existing binary file.
# if we know the size of the matrix we can do
z <- ff(filename=filename(y), vmode="double", dim=c(9,2), dimorder=c(2,1))
z
rm(z)

# If we only know the number of columns we can do
z <- ff(filename=filename(y), vmode="double")
# and set dim later
dim(z) <- c(length(z)/2, 2)
# Note that so far we have interpreted the file in major column order
z
# To interpret the file in major column order we set dimorder
# (a generalization for n-way arrays)
dimorder(z) <- c(2,1)
z

# removing the ff objects will trigger finalizer
# at next garbage collection
rm(x, ffcols, ffdafr, y, z)
gc()

# since we carefully selected the "close" finalizer,
# the files still exist
dir(path="/tmp", pattern="example_")

# now remove them physically
unlink(file.path("/tmp", dir(path="/tmp", pattern="example_")))

--
GRATIS für alle GMX-Mitglieder: Die maxdome Movie-FLAT!

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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: deparse() and the 'else' statement

2009-11-09T15:48:23Z

On 09/11/2009 4:20 PM, Yihui Xie wrote:

> Hi all,
>
> It is recommended in ?'if' that we use 'else' right after '}' instead
> of starting a new line, but I noticed deparse() will separate '}' and
> 'else' when the 'if...else' clause is used inside {...} (e.g. function
> body). Here is an example:
>
> ## if/else inside {}
>> cat(deparse(parse(text='function(){if (TRUE) {1} else {2}}')), sep='\n')
> structure(expression(function() {
> if (TRUE) {
> 1
> }
> else {
> 2
> }
> }), srcfile = <environment>)
> ## no {}
>> cat(deparse(parse(text='if (TRUE) {1} else {2}')), sep='\n')
> structure(expression(if (TRUE) {
> 1
> } else {
> 2
> }), srcfile = <environment>)
>
> I wonder if there is a way to let 'else' stay with '}' when deparsing
> the expression. Thanks!

No, there isn't. You're welcome to submit a well-tested patch, but
please accompany it with a stronger argument in its favour. Just
because humans are advised to type their code one way doesn't mean R
should, as long as it parses correctly.

Duncan Murdoch

>
>> sessionInfo()
> R version 2.10.0 (2009-10-26)
> i386-pc-mingw32
>
> locale:
> [1] LC_COLLATE=Chinese_People's Republic of China.936
> [2] LC_CTYPE=Chinese_People's Republic of China.936
> [3] LC_MONETARY=Chinese_People's Republic of China.936
> [4] LC_NUMERIC=C
> [5] LC_TIME=Chinese_People's Republic of China.936
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] animation_1.0-8 MASS_7.3-3
>
> loaded via a namespace (and not attached):
> [1] tools_2.10.0
>
> Regards,
> Yihui
> --
> Yihui Xie <xieyihui@...>
> Phone: 515-294-6609 Web: http://yihui.name
> Department of Statistics, Iowa State University
> 3211 Snedecor Hall, Ames, IA
>
> ______________________________________________
> 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.

Re: Complicated For Loop (to me)

2009-11-09T15:33:17Z

I think that we probably need a sample database of your original data.
A few lines of the dataset would probably be enough as long as it was fairly representative of the overall data set. See ?dput for a way of conveniently supply a sample data set.

Otherwise off the top of my head, I would think that you could just put all your subsets into a list and use lapply but I'm simply guessing without seeing the data.

--- On Mon, 11/9/09, agm. <amurray@...> wrote:

> From: agm. <amurray@...>
> Subject: Re: [R] Complicated For Loop (to me)
> To: r-help@...
> Received: Monday, November 9, 2009, 3:18 PM
>
> I've looked through ?split and run all of the code, but I
> am not sure that I
> can use it in such a way to make it do what I need.
> Another suggestion was
> using "lists", but again, I am sure that the process can do
> what I need, but
> I am not sure it would work with so many observations.
>
> I might have been too simple in my code. Let me try
> to explain it more
> clearly:
>
> I've got a data set of 4500 observations. I have
> already subset it into
> race/ethnicity (which I did by simple code). Now I
> needed to subset each
> race/ethnicity again into 9 separate regions. I again
> did this by simple
> code.
>
> The problem is now, I need to calculate a percentage for
> three different
> variables for all 9 regions for each race. I was
> trying to do this through
> a loop command.
>
> So a snippet of my code is :
>
> names <- c("white", "black", "asian", "hispanic")
> for(j in 1:length(names)){
> for(i in 1:9){
> names[j].cd[i].es.wash <- subset(names[j].cd[i],
> SLUNCH==1)
> es.cd[i].names.w <-
> sum(names.cd[i].es.wash$NWEIGHT)/sum(names.cd[i]$NWEIGHT)
> }
> }
>
>
> Maybe that makes it clearer. If not, I
> apologize. Thanks for the help that
> I have already received. It is greatly appreciated.
>
> Tony
>
> --
> View this message in context: http://old.nabble.com/Complicated-For-Loop-%28to-me%29-tp26269479p26272994.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.
>

__________________________________________________________________
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Re: Getting Sphericity Tests for Within Subject Repeated Measure Anova (using "car" package)

2009-11-09T15:29:07Z

No luck as in...? What error did you encounter?

In your example data set, you only have 2 levels of each within-Ss
factor, in which case you shouldn't expect to obtain tests of
sphericity; as far as I understand it, sphericity necessarily holds
when for repeated measures with only 2 levels and tests are really
only possible for repeated measures with 3 or more levels.

I think it's analogous to how you don't need to test homogeneity of
variance when performing a paired t-test; the test ends up
representing the pairs as single distribution of difference scores
with a single variance.

Mike

On Mon, Nov 9, 2009 at 5:30 PM, Sergios (Sergey) Charntikov
<sergioschr@...> wrote:

> Tried EZanova, no luck with my particular dataset.
>
>
> Sincerely,
>
> Sergios Charntikov (Sergey), MA
>
> Behavioral Neuropharmacology Lab
> Department of Psychology
> University of Nebraska-Lincoln
> Lincoln, NE 68588-0308 USA
>
>
>
>
> On Mon, Nov 9, 2009 at 2:25 PM, Mike Lawrence <Mike.Lawrence@...> wrote:
>>
>> Have you tried ezANOVA from the ez pacakge? It attempts to provide a
>> simple user interface to car's ANOVA (and when that fails, aov).
>>
>> On Mon, Nov 9, 2009 at 1:44 PM, Sergios (Sergey) Charntikov
>> <sergioschr@...> wrote:
>> > Hello everyone,
>> >
>> > I am trying to do within subjects repeated measures anova followed by
>> > the
>> > test of sphericity (sample dataset below).
>> > I am able to get either mixed model or linear model anova and TukeyHSD,
>> > but
>> > have no luck with Repeated-Measures Assuming Sphericity or Separate
>> > Sphericity Tests.
>> > I am trying to follow example from "car" package, but it seems that I am
>> > not
>> > getting something right.
>> >
>> >> Dataset$Sessn <- as.factor(Dataset$Sessn)
>> >
>> >> LinearModel.1 <- lm(Response ~ Sessn*Trtmt, data=Dataset)
>> >
>> >> summary(LinearModel.1)
>> >
>> > All, good so far, but I have problem understanding "idata=" and
>> > "idesign="
>> > functions pertaining to my example. Session is my repeated measure
>> > (Sessn 1
>> > and Sessn 2 = two sessions, in reality I have more) and it is already
>> > stacked. Any help or guidance on this matter.
>> >
>> > Thank you, my mock dataset is below. Each subject has two levels of
>> > treatment throughout four calendar days which are recoded to Session 1
>> > and
>> > Session 2 in order to compare treatments by the first and subsequent
>> > days of
>> > exposure (Treatment x Session; my DV is Response; Session is repeated).
>> >
>> > Subj Trtmt Sessn Response 1 N 1 5 1 D 1 6 1 N 2 4 1 D 2 7 2 N 1
>> > 8
>> > 2 D 1 9 2 N 2 2 2 D 2 1 3 N 1 4 3 D 1 5 3 N 2 6 3 D 2 2 4 N 1 5
>> > 4 D
>> > 1 6 4 N 2 4 4 D 2 7 5 N 1 8 5 D 1 9 5 N 2 2 5 D 2 1 6 N 1 4 6 D
>> > 1 5
>> > 6 N 2 6 6 D 2 2
>> >
>> > Sincerely,
>> >
>> > Sergios Charntikov (Sergey), MA
>> >
>> > Behavioral Neuropharmacology Lab
>> > Department of Psychology
>> > University of Nebraska-Lincoln
>> > Lincoln, NE 68588-0308 USA
>> >
>> > sergioschr-at-gmail-dot-com
>> >
>> > [[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.
>> >
>> >
>>
>>
>>
>> --
>> 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. ~
>
>

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

Java and R

2009-11-09T15:16:21Z

Hello,
I'm looking for any manual about using Java and R for begginers. Do you know any?