date:20110822

[R] Ignoring loadNamespace errors when loading a file

2011-08-22 Thread Allan Engelhardt

On a Unix machine I ran caret::rfe using the multicore package, and I 
saved the resulting object using save(lm2, file = lm2.RData).  
[Reproducible example below.]


When I try to load(lm2.RData) on my Windows laptop, I get

Error in loadNamespace(name) : there is no package called 'multicore'

I completely understand the error and I would like to ignore it and 
still load the saved object data.


I imagine that I can make myself an empty multicore package for Windows 
and load the data file successfully, but is there another way?


(I am not going to use any multicore functionality; I just want to 
inspect some of the data stored in the object and the reference in 
question is just an unfortunately stored link from the original call.)


(I did search for this question in the archives, but could only find it 
discussed in connection with starting R with a .RData file where the 
consensus seems to be to start R in vanilla mode and install the missing 
package.  This situation is different and installing a Unix-only package 
on Windows is obviously a non-starter, except as I proposed above.)


Obligatory reproducible example: On the Unix machine do

library(multicore)
a - list(data = 1:10, fun = mclapply)
save(a, file = a.RData)

and then try to load the a.RData file on Windows.  The question is if 
I can recover the data (1:10) on that platform.


Allan


 sessionInfo()

R version 2.13.1 (2011-07-08)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252

attached base packages:
[1] compiler  stats graphics  grDevices utils datasets  methods
[8] base

other attached packages:
[1] caret_4.98  cluster_1.14.0  reshape_0.8.4   plyr_1.6
[5] lattice_0.19-31 boot_1.3-2  ctv_0.7-3

loaded via a namespace (and not attached):
[1] grid_2.13.1  tools_2.13.1

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

Re: [R] Changing data scales

2011-08-22 Thread Jim Lemon


On 08/22/2011 10:04 AM, Jim Silverton wrote:

I have data that ranges from 0.3 to 2 and I want to change the scale to be
from 0 to 1.
Can this be done in R?


Hi Jim,

library(prettyR)
rescale(x,c(0,1))

will linearly transform x into the specified range.

Jim

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Re: [R] I have a problem with R!!

2011-08-22 Thread Petr PIKAL

Hi

Your code is rather baroque and it is difficult to see what is exactly 
going on without having appropriate data. I does not consider your process 
of reading data from Excel problematic.

Maybe the difference is in that

d- rnorm(whatever) 

produces vector while

d- read.delim(clipboard, header = T, dec = ,)

produces data frame

you can subset vector by

d[indices]

but doing the same with data.frame you try to subset columns which, in 
your case should not be what you want.

at least showing us 

str(data.readed.from.Excel)

can help us to help you.

Regards
Petr

 
 On 08/21/2011 05:29 AM, nferr...@fceia.unr.edu.ar wrote:
  Dear all
 
  i´m working with a program i´ve made in R (using functions that others
  created)
 
  to run my program i need a sample. if i generate the sample using  for
  example, rnorm(n, mu, sigma) i have no problem
 
  but if i obtain a sample from a column in excel and i copy it, the 
program
  says that there is a mistake: it says Error en `[.data.frame`(data,
  indices) : undefined columns selected
 
  my program is:
 
  d- read.delim(clipboard, header = T, dec = ,)
  #Para determinar los valores de las componentes del vector de 
capacidad es
  necesario definir primero las especificaciones y el valor objetivo, T, 
así
  como el máximo valor admitido para la proporción de producción no
  conforme, a cada lado de los límites de especificaciones#
  # Ingrese ahora el valor del límite inferior de especificaciones#
  LIE- 13
  # Ingrese ahora el valor del límite superior de especificaciones#
  LSE- 17
  # Ingrese ahora el valor objetivo#
  T- 14.5
  # Ingrese ahora el máximo valor admitido para la proporción de 
producción
  no conforme a cada lado de los límites de especificaciones#
  MA- 0.00135
  D- min ((LSE-T), (T-LIE))
  compo1- function(data, indices)
  {
  d- data[indices]
  n = length (d)
  desvio- sd(d)
  y- rep(1:n)
  y[x= mean(d)]- 1
  y[xmean(d)]- 0
  RI1- D/(3*desvio*2*mean(y))
  RI2- D/(3*desvio*2*(1-mean(y)))
  return (min (RI1, RI2))
  }
  compo2- function(data, indices)
  {
  d- data[indices]
  c2- (abs(mean(d) - T))/D
  return (1-c2)
  }
  compo3-function(data, indices)
  {
  d- data[indices]
  n- length (d)
  y- rep(1:n)
  y[d  LIE]- 1
  y[d= LIE]- 0
  INFE- mean (y);
  y- rep(1:n)
  y[d  LSE]- 1
  y[d= LSE]- 0
  SUPE- mean (y);
  PPI- (1 - INFE)/(1-MA)
  PPS- (1 - SUPE)/(1-MA)
  return (min (PPI, PPS))
  }
  save(file = compo1.RData)
  save(file = compo2.RData)
  save(file = compo3.RData)
  compos- function(data, indices)
  {
  d- data[indices]
  capacidad- c(compo1(d), compo2(d), compo3(d))
  return(capacidad)
  }
  save(file = compos.RData)
  require (boot)
  vectorcapacidad- boot (d, compos, R = 3000)
 
  ETC. ETC.
 
 
 
  WHEN I START MY PROGRAM WRITING:
  d- rnorm (n, mu, sigma)
 
  I HAVE NO PROBLEM. BUT WHEN I READ A VECTOR FROM EXCEL, R TELLS ME
  Error en `[.data.frame`(data, indices) : undefined columns selected
 
 
  CAN YOU HELP ME THANK YOU VERY MUCH!
 
  NOEMI FERRERI, ROSARIO, ARGENTINA
  SCHOOL OF INDUSTRIAL ENGINEERING
 
 Hi Noemi,
 Without some sample data, I can only guess, but I would first try saving 

 the Excel spreadsheet in CSV format and then reading the data in with 
 read.csv. This might solve your problem.
 
 Jim
 
 __
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 PLEASE do read the posting guide 
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Re: [R] Quadratic equation

2011-08-22 Thread Uwe Ligges




On 22.08.2011 03:34, Brad Patrick Schneid wrote:

these guys wont help you with your homework.  But have you ever heard of
Google?... if so, R has plenty of online manuals and cheat sheets.



1. This went to R-help (and hence not necessarily to the original poster).
2. You forgot to cite the original question.

Uwe Ligges



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View this message in context: 
http://r.789695.n4.nabble.com/Quadratic-equation-tp3758790p3759239.html
Sent from the R help mailing list archive at Nabble.com.

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

Re: [R] Wiki/revision control to management of CRAN package repository

2011-08-22 Thread Philippe Grosjean


Hello,

For the R Wiki, not very realistic to think about fusing it with the 
other tools, due to the nature of a wiki on one hand, and the necessity 
to share the CRAN site across different repositories on the other hand.


Also, I think that packages development is in the hand of their 
respective developers/maintainers. Except for making sure they pass R 
CMD check and respect a limited amount of requirements (license, size of 
enclosed documents, ...), it is probably not a good idea to FORCE people 
to collaborate, or share the same point of view for a given R extension. 
There are sometimes very different implementations of the same concept, 
e.g., object oriented approaches in S3/S4 (official), but see also R.oo 
or proto for alternative ideas. Another example: RUnit vs svUnit vs 
testthat. Would not be a good idea to force all these people to share 
the same implementation and fuse everything in a single package,... 
unless they decide by themselves, and completely freely, to do so.


This is an Open Source community, and it evolves a little bit like 
natural living ecosystems... with different ideas that could emerge and 
are ultimately selected by a kind of natural selection mechanism, 
related to (1) adoption of one or the other tool by the R community, and 
(2) the energy put in the project by their developers to maintain or 
make it better suitable to the community. That mechanism can only work 
if you are not too rigid in constraints for R packages.


The drawback is a little bit of confusion for the end-user that does not 
always easily know which of the different implementations he should adopt.


Best,

Philippe Grosjean


On 22/08/11 06:02, Etienne Low-Décarie wrote:

I propose the following humbly, with little know how as to how to implement, 
and realize it may have been proposed many times.  It is just something I had 
on my mind.

Would it be possible/desirable to have the whole CRAN package repository 
accessible through a public wiki, forge or version control interface (ideally a 
fusion of the wiki and forge approach)?


It appears it would be a first for a software repository.

CRAN package repository is becoming a jungle of R code and may do well with 
currating and editorial effort.  This can not/should not be the task of a 
single person or small group of people.  Using a crowd sourced method by 
implementing a wiki approach to the CRAN package repository would allow for the 
rapid editing, sorting and improvement of this impressive and precious 
resource, while also improving the accessibility, visibility and quality of 
individual packages.  It would also bind the

For example, such an interface would allow the cleaning up of the repository 
through the use of tagging of packages, using similar approaches to the 
wikipedia project 
(http://en.wikipedia.org/wiki/Wikipedia:Template_messages#Cleanup).

Such a tagging approach could be used within existing vcs, if the repository 
was migrated/mirrored within one of these systems.


Packages could be marked using tags for all following actions prior to 
implementing the action.  Actions could be undertaken directly by package users 
after a delay or discussion.

Packages management/editorial effort:
-Merging/
-Combining packages that have:
-Large overlap in functionality
-Are largely interdependant
-Are only minor extensions of another package
-…
-Split/fork
-Subdividing behemoth packages into smaller packages with more 
specific tasks
-Categorization
-Packages could be sorted by use, improvement of Task View
-Tags, keywords could be added to packages for searching
-Packages could be placed in a hierarchy, not only by true 
dependance and reverse dependance, but also by logical dependance/reverse 
dependance
-ie. which package should probably be used with which 
package, an improvement on the see also help section
-Deletion
-Marking/tagging
-a stub/prototype
-broken
Package improvements
-Improving help files
-Adding functions
-Adding examples
-Requiring, improving or adding references
-References to the theory or approach used...
-A section could include a list of articles making use of the 
package, with package users encourage to enter this information
-This would allow package author recognition and allow a 
package impact factor
-Adding key words for indexing and searching
-Function improvement
-Adding compatibility with other packages/formats (including 
when merging packages)
-Speed improvements
Discussion
-On package improvements, management steps directly attached to the 
package
-Help discussion

Re: [R] Ignoring loadNamespace errors when loading a file

2011-08-22 Thread Uwe Ligges




On 22.08.2011 08:52, Allan Engelhardt wrote:

On a Unix machine I ran caret::rfe using the multicore package, and I
saved the resulting object using save(lm2, file = lm2.RData).
[Reproducible example below.]

When I try to load(lm2.RData) on my Windows laptop, I get

Error in loadNamespace(name) : there is no package called 'multicore'

I completely understand the error and I would like to ignore it and
still load the saved object data.

I imagine that I can make myself an empty multicore package for Windows
and load the data file successfully, but is there another way?

(I am not going to use any multicore functionality; I just want to
inspect some of the data stored in the object and the reference in
question is just an unfortunately stored link from the original call.)

(I did search for this question in the archives, but could only find it
discussed in connection with starting R with a .RData file where the
consensus seems to be to start R in vanilla mode and install the missing
package. This situation is different and installing a Unix-only package
on Windows is obviously a non-starter, except as I proposed above.)

Obligatory reproducible example: On the Unix machine do

library(multicore)
a - list(data = 1:10, fun = mclapply)
save(a, file = a.RData)

and then try to load the a.RData file on Windows. The question is if I
can recover the data (1:10) on that platform.


Short answer: No.

The element fun within a relies on the Namespace being present. And 
then you want additionally just part of an object. If this is really 
relevant, you have to digg into ./src/main/saveload.R and serialize.R 
and try to write your own interface to extract parts of objects. So this 
is probably not worth the effort.


Uwe Ligges




Allan


sessionInfo()

R version 2.13.1 (2011-07-08)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252

attached base packages:
[1] compiler stats graphics grDevices utils datasets methods
[8] base

other attached packages:
[1] caret_4.98 cluster_1.14.0 reshape_0.8.4 plyr_1.6
[5] lattice_0.19-31 boot_1.3-2 ctv_0.7-3

loaded via a namespace (and not attached):
[1] grid_2.13.1 tools_2.13.1

__
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.


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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.

Re: [R] Multiple R linear models into one Latex table

2011-08-22 Thread Rmh

please look at the latex() function in package Hmisc.

Sent from my iPhone

On Aug 22, 2011, at 0:55, Alex Ruiz Euler rruizeu...@ucsd.edu wrote:

 
 
 Dear community,
 
 I had been looking for an easy way to produce latex tables from R
 output. xtable() and the package apsrtable produce good outputs but they are 
 not
 exactly what I was looking for. 
 
 I wrote this code that generates regression tables from multiple R
 linear models. I want to share it because it might be
 useful for someone else, and because I would appreciate comments on how to
 optimize the code. I'm also open to suggestions about design or packages to 
 include. Or pointing out bugs. I hope this is the right list to post.
 
 Please consider it is still in a very primitive form, and the generated table 
 might look strange for some disciplines (I come from political science).
 
 Thanks,
 Alex
 
 
 
 ## Start script
 
 
 #
  R models to Latex table 
 #
 
 
 # nice() - Multiple R linear models into one Latex table.
 #
 #nice(list(fit1,fit2,...,fitn)) for generic table with n models
 #
 #or
 #
 #  for better tables, create objects 'model.names' and 'final.varnames', then
 #
 #nice(list(fit1,fit2,...,fitn), model.names, final.varnames)
 
 
 
 nice - function(modelos, model.names=NULL, final.varnames=NULL) {
 
  var.names-vector(mode=character)
  k-1
  for (i in 1:length(modelos)) {
l-length(names(coef(modelos[[i]])))
var.names[c(k:c(k+l-1))]-names(coef(modelos[[i]]))
k-k+l
  }
  var.names-unique(var.names)
 
  if(is.null(final.varnames)) {
  final.varnames-var.names
  }
 
  if(is.null(model.names)) {
  model.names-paste(Model,1:length(modelos))
  }
 
  mat.all-matrix(data=NA, nrow=length(var.names)*2, byrow=FALSE, 
 ncol=length(model.names), 
 dimnames=list(c(rep(c(coef,sd),length=length(var.names)*2)), 
 c(model.names)))
  dimnames(mat.all)[[1]][c(seq(1,dim(mat.all)[1], by=2))]-var.names; 
 dimnames(mat.all)[[1]][c(seq(2,dim(mat.all)[1], by=2))]-
 
  for (j in 1:length(modelos)) {

 mat.all[c(match(names(coef(modelos[[j]])),dimnames(mat.all)[[1]])),j]-coef(modelos[[j]])

 mat.all[c(match(names(coef(modelos[[j]])),dimnames(mat.all)[[1]])+1),j]-sqrt(diag(vcov(modelos[[j]])))
  }
 
  mat.all-signif(mat.all, digits=3); 
 dimnames(mat.all)[[1]][c(seq(1,dim(mat.all)[1], by=2))]-final.varnames
 
 
  cat(\\begin{table}[!hbt], \n,\\caption[Short for List of Tables]{Long 
 for this 
 title},\n,\\begin{center},\n,\\begin{tabular}{ll},\n)
  
 cat(,paste(model.names[1:length(model.names)-1],),model.names[length(model.names)],,\n,\\hline,\n)
  for(i in 1:dim(mat.all)[1]) {
if(i %in% c(seq(1,dim(mat.all)[1], by=2))) { # Odd rows (coefficients)
 
  cat(dimnames(mat.all)[[1]][i],)
  cat(
for (j in 1:(length(modelos)-1)) {
  if(!is.na(mat.all[i,j])) {
p.val-abs(mat.all[i,j]/mat.all[i+1,j])
if(p.val1.644) {
  if(1.645=p.val  p.val1.96) cat(\\textbf{,round(mat.all[i,j], 
 digits=5),*}, sep=) else {
if(1.96p.val  p.val2.576) cat(\\textbf{,round(mat.all[i,j], 
 digits=5),**}, sep=) else {
  cat(\\textbf{,round(mat.all[i,j], digits=5),***}, sep=)
}
  }
  } else cat(round(mat.all[i,j], digits=5),)
} else cat()
  })
 
  cat(
for (j in length(modelos) ) {
  if(!is.na(mat.all[i,j])) {
p.val-abs(mat.all[i,j]/mat.all[i+1,j])
if(p.val1.644) {
  if(1.645=p.val  p.val1.96) cat(\\textbf{,round(mat.all[i,j], 
 digits=5),*}, sep=) else {
if(1.96p.val  p.val2.576) cat(\\textbf{,round(mat.all[i,j], 
 digits=5),**}, sep=) else {
  cat(\\textbf{,round(mat.all[i,j], digits=5),***}, sep=)
}
  }
} else cat(round(mat.all[i,j], digits=5))
}
   })
  cat(, \n )
 
  } else {
  
 cat(,ifelse(!is.na(mat.all[i,1:(length(dimnames(mat.all)[[2]])-1)]),paste((\\emph{,round(mat.all[i,1:(length(dimnames(mat.all)[[2]])-1)],
  digits=5),}),sep=), paste()),
   
 ifelse(!is.na(mat.all[i,length(dimnames(mat.all)[[2]])]),paste((\\emph{,round(mat.all[i,length(dimnames(mat.all)[[2]])],
  digits=5),}),sep=),paste( )),
, sep=, fill=TRUE)}
}
 
  cat(\\hline,\n,\\emph{n},)
  cat(
  for(j in 1:(length(modelos)-1)) {
cat(length(modelos[[j]]$residuals),,sep=)
  },
  for(j in length(modelos)) {
cat(length(modelos[[j]]$residuals), , \n, sep=)
  }
  )
 
  cat(\\emph{Adj-R$^2$})
  cat(
for(j in 1:(length(modelos)-1)) {
if (class(modelos[[j]])[1]==lm) 
 {cat(round(summary(modelos[[j]])$adj.r.squared,digits=2),, sep=)} else {
cat() 
}
  },
  for(j in length(modelos)) {
if (class(modelos[[j]])[1]==lm) 
 {cat(round(summary(modelos[[j]])$adj.r.squared,digits=2),,\n)} else {
cat(,\n) 
}
  } 
)
 
  cat(\\emph{AIC})
  cat(
for(j in 1:(length(modelos)-1)) {
if (class(modelos[[j]])[1]==glm) {cat(round(summary(modelos[[j]])$aic, 
 digits=0),, sep=)} else {
cat()

Re: [R] questions about metafor package

2011-08-22 Thread Mike Cheung

Hi, Emilie.

For your second question. You may check Gleser and Olkin (2009). They gave
several formulas to estimate the sampling covariance for dependent effect
sizes. One of them can be applied in your case.

Gleser, L. J., Olkin, I. (2009). Stochastically dependent effect sizes. In
H. Cooper, L. V. Hedges, J. C. Valentine (Eds.), The handbook of research
synthesis and meta-analysis. (2nd ed., pp. 357-376). New York: Russell Sage
Foundation.

Regards,
Mike
--
-
Mike W.L. Cheung Phone: (65) 6516-3702
Department of Psychology Fax: (65) 6773-1843
National University of Singapore
http://courses.nus.edu.sg/course/psycwlm/internet/
-

On Sat, Aug 20, 2011 at 10:19 PM, Michael Dewey i...@aghmed.fsnet.co.ukwrote:

At 16:21 17/08/2011, Emilie MAILLARD wrote:

Hello,
Ã
I would like to do a meta-analysis with the package ÃÂ«Ã metaforÃ ÃÂ».
Ideally I would like to use a mixed model because IÃ¢â¬â¢m interested to
see
the effect of some moderators. But the data set I managed to collect from
literature presents two limits.
Ã
-Ã Ã Ã Ã Ã Ã Ã Ã Firstly, for each observation, I have means for a
treatment and for a control, but I donÃ¢â¬â¢t always have corresponding
standard deviations (52 of a total of 93 observations donÃ¢â¬â¢t have
standard
deviations). Nevertheless I have the sample sizes for all observations so I
wonder if it was possible to weight observations by sample size in the
package ÃÂ«Ã metaforÃ ÃÂ».
-Ã Ã Ã Ã Ã Ã Ã Ã Secondly, some observations are probably not
independent
as I have sometimes several relevant observations for a same design. More
precisely, for these cases, the control mean is identical but treatment
means varied. Ideally, I would not like to do a weighted average for these
non-independent observations because these observations represent levels of
a moderator. I know that the package ÃÂ«Ã metaforÃ ÃÂ» is not designed
for the
analysis of correlated outcomes. What are the dangers of using the package
even if observations are not really independent ? Ã

Emilie,
I am not sure whether this is the answer to your problem of observations
which are not independent but you might also look at the metaSEM package
http://courses.nus.edu.sg/**course/psycwlm/internet/**metaSEM/http://courses.nus.edu.sg/course/psycwlm/internet/metaSEM/
I am still trying to understand his paper on this (see link for reference)
but he is trying to embed meta-analysis within the structural equation
framework and it may be possible to cope with lack of independence in that
way. But as I say I am still trying to come to grips with the paper.

Thank you for your help,
Ã
Ãâ°milie.
[[alternative HTML version deleted]]

Michael Dewey
i...@aghmed.fsnet.co.uk
http://www.aghmed.fsnet.co.uk/**home.htmlhttp://www.aghmed.fsnet.co.uk/home.html

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[[alternative HTML version deleted]]

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