Re: [R] Ubuntu 2.9.2 packages broken for amd64
Hi Dirk, Thank you for your prompt reply. The issue has been actually resolved in just a few hours after I posted it. Many apologies for being too impatient! Best, Ivan On Thursday 17 September 2009 10:15:04 pm Dirk Eddelbuettel wrote: > Ivan, > > On 17 September 2009 at 21:22, Ivan Adzhubey wrote: > | Attempt to install R 2.9.2 on Ubuntu 9.04 results in errors: > | > | $ sudo apt-get install r-base > | Reading package lists... Done > | Building dependency tree > | Reading state information... Done > | Some packages could not be installed. This may mean that you have > | requested an impossible situation or if you are using the unstable > | distribution that some required packages have not yet been created > | or been moved out of Incoming. > | The following information may help to resolve the situation: > | > | The following packages have unmet dependencies: > | r-base: Depends: r-base-core (>= 2.9.2-2jaunty1) but 2.9.2-2jaunty0 is > | to be installed > | Depends: r-recommended (= 2.9.2-2jaunty1) but it is not going > | to be installed > | E: Broken packages > | > | This was on amd64 system. I have tried several mirrors and the same error > | persisted. Inspecting package repos by eye shows r-*-2.9.2-2jaunty1 > | packages for amd64 architecture are missing while they are present for > | i386. > > I noticed that too and spoke with Vincent and Michael -- this should get > sorted out 'soon'. It is due to the non-binary packages being ready from > the i386 build but being dependent on the corresponding amd64 packages > which are not there yet. > > As a one-off alternative, you could preview the packages we prepared for > the upcoming Ubuntu 9.10 release. See this page > https://launchpad.net/~revor/+archive/ppa > for details and the sources.list entry. These contain some REvolution R > enhancements. I run those on amd64 at work, and they do install and run > just fine :) > > Lastly, r-sig-debian is a better list for this. I just caught your post by > chance. > > Regards, Dirk > __ 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.
[R] Ubuntu 2.9.2 packages broken for amd64
Hi, Attempt to install R 2.9.2 on Ubuntu 9.04 results in errors: $ sudo apt-get install r-base Reading package lists... Done Building dependency tree Reading state information... Done Some packages could not be installed. This may mean that you have requested an impossible situation or if you are using the unstable distribution that some required packages have not yet been created or been moved out of Incoming. The following information may help to resolve the situation: The following packages have unmet dependencies: r-base: Depends: r-base-core (>= 2.9.2-2jaunty1) but 2.9.2-2jaunty0 is to be installed Depends: r-recommended (= 2.9.2-2jaunty1) but it is not going to be installed E: Broken packages This was on amd64 system. I have tried several mirrors and the same error persisted. Inspecting package repos by eye shows r-*-2.9.2-2jaunty1 packages for amd64 architecture are missing while they are present for i386. --Ivan __ 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.
Re: [R] x86 SSE* Pointer Favors
Hi Ivo, On Friday 13 June 2008 12:23:06 am ivo welch wrote: > Dear Statisticians--- This is not even an R question, so please > forgive me. I have so much ignorance in this matter that I do not > know where to begin. I hope someone can point me to documentation > and/or a sample. You will sure find some answers to your questions if you look into R-admin.html file under "Building from source" section. Do a search on BLAS and you will be presented with some options. Using a bit of R web site search on the same keyword will give you even more food for thought. > I want to compute a covariance as quickly as non-humanly possible on > an Intel core processor (up to SSE4) under linux. Alas, I have no > idea how to engage CPU vectorization. Do I need to use special data > types, or is "double" correct? Does SSE* understand NaN? Should I > rely on gcc autodetection of the vectorized meaning of my code, or are > there specific libraries that I should call? I use Goto BLAS library and it works great. Usually runs 3 to 30 times faster than the stock R BLAS library, depending on your code. Enabling SSE instructions in addition while building R (yes, you have to enable them explicitly, see man gcc) is possible but does not help much since all maths is mostly done in BLAS. That said, optimized BLAS libraries give most speed increase with older processors. Newer crop of multi-core CPUs with large shared caches is much more difficult to hand-tune code for. You may want to subscribe to Goto BLAS mailing list for an in-depth discussion. ATLAS community is also very helpful (I use their code with our AMD CPUs). > What I want to learn about is as simple as it gets: > typedef double Double; // or whatever SSE* needs as close equivalent > Double vector1[N], vector2[N]; > // then fill them with stuff. R does not have types, everything that does not look like character string or an integer is treated as double. All arithmetics are always done in double precision. > vector3= vector_mult(vector1,vector2, N); > vector4= sum(vector1, N); > > I just need a pointer and/or primer. PS: If someone knows of a > superfast vectorized implementation of Gentleman's WLS algorithm, > please point me to it, too. I am still using my old non-vectorized C > routines. HTH, Ivan __ 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.
Re: [R] Comparing two groups of proportions
Hi Rolf,
On Monday 09 June 2008 11:16:57 pm Rolf Turner wrote:
> Your approach tacitly assumes --- as did the poster's question --- that
> the probability of passing an item by one method is *independent* of
> whether it is passed by the other method. Which makes the methods
> effectively independent of the nature of the item being assessed!
So it seems I can't just block my primary factor (QA procedure) by nuisance
one (production line) and run Cochran test to see if effects of primary
factor are identical for both its levels.
> Not much actual quality being assured there!
In fact, I am not interested in quality of QA procedures as much as in how
different the results are (error component).
Thanks,
Ivan
> cheers,
>
> Rolf Turner
>
> On 10/06/2008, at 2:57 PM, Greg Snow wrote:
> > here is one approach:
> >
> > res <- cbind( c(10, 5, 1, 12, 3, 8, 7, 2, 10, 1),
> > c(90,15,79,38,7,92,13,78,40,9) )
> >
> > line <- gl(5,1,length=10, labels=LETTERS[1:5])
> >
> > qa <- gl(2,5)
> >
> > fit <- glm( res ~ line*qa, family=binomial )
> >
> > summary(fit)
> >
> > anova(fit, test='Chisq')
> >
> > The interaction terms measure the difference between the different
> > combinations of QA method and production line, if they are all 0,
> > then that means the effect of QA is the same accross production
> > lines and the qa main effect measures the difference between the 2
> > methods (allowing for differences in the prodoction lines), testing
> > if that equals 0 should answer your question.
> >
> > Hope this helps,
> >
> >
> >
> > From: [EMAIL PROTECTED] [EMAIL PROTECTED]
> > On Behalf Of Ivan Adzhubey [EMAIL PROTECTED]
> > Sent: Monday, June 09, 2008 4:28 PM
> > To: r-help@r-project.org
> > Subject: [R] Comparing two groups of proportions
> >
> > Hi,
> >
> > I have a seemingly common problem but I can't find a proper way to
> > approach
> > it. Let's say we have 5 samples (different size) of IC circuits
> > coming from 5
> > production lines (A, B, C, D, E). We apply two different non-
> > destructive QA
> > procedures to each sample, producing to sets of binary outcomes
> > (passed:
> > no/yes). So, we have two groups of proportions:
> >
> > QA1 QA2
> > no/yes no/yes
> > A 10/90 8/92
> > B 5/157/13
> > C 1/792/78
> > D 12/38 10/40
> > E 3/7 1/9
> >
> > How would I test if the two QA procedures in question give
> > significantly
> > different results, at the same time controlling for the possible
> > production
> > line contribution? It looks like there are many variants of multiple
> > proportions tests available in R and various extra packages but
> > none seems to
> > exactly fit this very simple problem. I would appreciate any advice.
> >
> > Thanks,
> > Ivan
> >
> > The information transmitted in this electronic communica...
> > {{dropped:10}}
> >
> > __
> > R-help@r-project.org 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.
> >
> > __
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
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> > 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
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[R] Comparing two groups of proportions
Hi,
I have a seemingly common problem but I can't find a proper way to approach
it. Let's say we have 5 samples (different size) of IC circuits coming from 5
production lines (A, B, C, D, E). We apply two different non-destructive QA
procedures to each sample, producing to sets of binary outcomes (passed:
no/yes). So, we have two groups of proportions:
QA1 QA2
no/yes no/yes
A 10/90 8/92
B 5/157/13
C 1/792/78
D 12/38 10/40
E 3/7 1/9
How would I test if the two QA procedures in question give significantly
different results, at the same time controlling for the possible production
line contribution? It looks like there are many variants of multiple
proportions tests available in R and various extra packages but none seems to
exactly fit this very simple problem. I would appreciate any advice.
Thanks,
Ivan
The information transmitted in this electronic communica...{{dropped:10}}
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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.
[R] Problem with approximate null distribution (package: coin)
Hi,
I am trying to make use of "approximate" option to obtain null distribution
through Monte-Carlo resampling, as described in coin library documentation.
Unfortunately, this does not play well with my data -- permutation process
swallows astonishingly large amounts of RAM (4-5Gb) and runs far too long (30
min for B=10). Apparently, this is caused by the size of my dataset (see
example below) but I was under impression that permutation algorithm just
draws random contingency tables from the fixed conditional marginals, in
which case the amount of memory required should not depend on the dataset
size very much, as well as the execution time should only depend on B.
Obviously, I was wrong about both assumptions. Is there any reasonable way to
work around these limitations in case of a large dataset? It's not that large
in fact, so I am a bit surprised the efficiency of resampling is so poor.
Below is the dataset example, what I am trying to do is perform cmh_test() on
a 4x2x3 table.
> adata
, , Content = low
Response
Time YesNo
0 384597259
1 585888039
2 621896102
31466 1606456
, , Content = medium
Response
Time YesNo
0 101 99525
1 160191698
2 173146814
3 469485012
, , Content = high
Response
Time YesNo
0 119175938
1 167163881
2 77131063
3 522548924
--Ivan
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Re: [R] Hmisc xYplot won't do conditioning on factors?
Hi,
Since nobody replied, I'd rather post this self-reply for the sake of
documenting the solution.
It appears that xYplot, unlike "standard" xyplot (or coplot to that matter)
does not accept factors as x variable in formula. With x converted to numeric
everything worked as expected. This small discrepancy was not documented on
xYplot help page.
--Ivan
On Tuesday 26 February 2008 07:47:14 pm Ivan Adzhubey wrote:
> Hi,
>
> I am trying to replace (lattice) standard xyplot with xYplot variant from
> Hmisc package to be able to add error bars to my plots. However, this does
> not work, e.g:
>
> library(lattice)
> d <- data.frame(
> SKU=gl(3, 1, 21, labels=c("a", "b", "c")),
> Weekday=gl(7, 3, 21),
> QCRate=runif(21))
>
> xyplot(QCRate ~ Weekday | SKU, data=d)
>
> (this plots nice 3 panels as per a,b,c conditionals)
>
> library(Hmisc)
>
> > xYplot(QCRate ~ Weekday | SKU, data=d)
>
> Error in Summary.factor(1:7, na.rm = TRUE) :
> range not meaningful for factors
>
> Is there a workaround?
>
> Thanks,
> Ivan
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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[R] Hmisc xYplot won't do conditioning on factors?
Hi,
I am trying to replace (lattice) standard xyplot with xYplot variant from
Hmisc package to be able to add error bars to my plots. However, this does
not work, e.g:
library(lattice)
d <- data.frame(
SKU=gl(3, 1, 21, labels=c("a", "b", "c")),
Weekday=gl(7, 3, 21),
QCRate=runif(21))
xyplot(QCRate ~ Weekday | SKU, data=d)
(this plots nice 3 panels as per a,b,c conditionals)
library(Hmisc)
> xYplot(QCRate ~ Weekday | SKU, data=d)
Error in Summary.factor(1:7, na.rm = TRUE) :
range not meaningful for factors
Is there a workaround?
Thanks,
Ivan
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