Re: [R] Changing UTC time to a time zone different from system time zone
On 28/12/2014 21:52, Jeff Newmiller wrote: Very few attachment file types are allowed through the mailing list, and yours did not make it. I noticed that your format strings did not appear consistent... see the format string I use below, and make sure the characters between the numeric parts of your format are correct for your case. Did you read the help file ?strptime ? Note that there are two steps... importing the character data to a POSIXct type, and then choosing how you want to display those instants of time in whatever time zones you wish. That is, regardless of time zone, the POSIXct value represents the same instant of time regardless of time zone you want to display it in, while POSIXlt will in general have different internal representations that match the external representations. Note that you really do need to read ?timezone, because the below code only works if your system understands the GMT and Australia/Darwin timezones, and that is operating-system-specific behavior. This was successfully run on Ubuntu... you did not mention your operating system. Correct, but for R = 3.1.0 that help page says 'Almost all R platforms make use of a time-zone database originally compiled by Arthur David Olson and now managed by IANA, in which the preferred way to refer to a time zone is by a location ...' and GMT is always supported although UTC is preferred. All the platforms documented in 'R Installation and Administration' do make such use; however occasionally we hear of new ports, hence the cautious use of 'Almost'. However, we were also not told the R version tst - 2014-12-25 00:00 # GMT tstct - as.POSIXct( tst, format=%Y-%m-%d %H:%M, tz=GMT ) tstct [1] 2014-12-25 GMT unclass(tstct) [1] 1419465600 attr(,tzone) [1] GMT tstlt - as.POSIXlt( tstct, tz=Australia/Darwin ) tstlt [1] 2014-12-25 09:30:00 ACST unclass(tstlt) $sec [1] 0 $min [1] 30 $hour [1] 9 $mday [1] 25 $mon [1] 11 $year [1] 114 $wday [1] 4 $yday [1] 358 $isdst [1] 0 $zone [1] ACST $gmtoff [1] 34200 attr(,tzone) [1] Australia/Darwin ACST ACDT tstct2 - as.POSIXct( tstlt ) tstct2 [1] 2014-12-25 09:30:00 ACST unclass(tstct2) [1] 1419465600 attr(,tzone) [1] Australia/Darwin On Sun, 28 Dec 2014, Faranak Golestaneh wrote: Dear Friends, I?ve just started using R. I am working on a database containing date-time as well as numeric values. Firstly I have changed the class of the data/time column from factor to POSIXlt. Time and date are based on UTC time but I need to change them to a local time ( not my local time, my zone is Singapore but the data are from Australia) so I need to change the time and date to Australia time say e.g AEST. I tried one way or another but all failed. The following is example of the command I used. Also one of my .CSV files is attached. Data_Power - read.table(train15.csv,header = TRUE, sep = ,, row.names = NULL); Date_clm=Data_Power$TIMESTAMP; Date_original - strptime(Date_clm, %Y %m %d %H:%M, tz=GMT) Date_local =format(Date_original, format=%c, tz=America/New_York) Date_local - strptime(Date_original, %Y %m %d %H:%M +0800) Date_local=as.POSIXlt(Date_original, Australia/Darwin) Date_local=as.POSIXlt(Date_original, AEST ) I would be so thankful if you help me out. Thanks Cheers, Faranak -- Brian D. Ripley, rip...@stats.ox.ac.uk Emeritus Professor of Applied Statistics, University of Oxford 1 South Parks Road, Oxford OX1 3TG, UK __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] RAxML using R
Hello, You can put the code of that function in a separate file, say raxml.R and then use source(raxml.R). See the help page ?source. Hope this helps, Rui Barradas Em 28-12-2014 23:09, Luigi Marongiu escreveu: Dear all, I would like to run RAxML for phylogenetic analysis as indicated on Paradis' Analysis of phylogenetic and evolution with R (p. 158) thus I tried to use the package phyloch and its function raxml(). I am using Linux Ubuntu 14 and I was able to successfully install RAxML on my machine. However phyloch is not supported by CRAN and the package as provided by the author on http://www.christophheibl.de/Rpackages.html cannot be installed -- or at least I could not using the Software centre. I found a function on http://www.christophheibl.de/raxml.R that should be the actual script of the function raxml(). However I do not know how to implement such function. Shall I just copy it into my script? But then I will have hundreds of lines of code that will mess with my script. Can I create a kind of package that I can call from my code? Or is there another way to send the data to the linux shell and call the raxml function? Could anybody help? Thank you Luigi __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] half-logistic distribution
On 12/26/2014 05:00 AM, r-help-requ...@r-project.org wrote: i want to analyse survival data using typeI HALF LOGISTIC DISTRIBUTION.how can i go about it?it installed one on R in the survival package didn't include the distribution...or i need a code to use maximum likelihood to estimate the parameter in survival analysis.a typical example of distribution other than that installed in R will help.thanks I am the author of the survival package, and had never heard of the type I half-logistic before. New distributions can be added to survreg() if they can be represented as location-scale families; I don't think that your distribution can be written in that way. Look at survival under the Task Views tab (upper left corner) of the cran.org web page -- someone else may have already done it. Terry T. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Legend (guides) for point and two line graph
Hi David, 1) set size to a fixed value instead of mapping it to a constant, i.e., geom_line(size = 2) instead of geom_line(aes(size = 2)) 2) perhaps ggplot(rtest, aes(x=Time, y=Calculated,color=Model, group=Model)) + geom_line(size = 2) + geom_point(aes(y=Observed, shape=), size=6, colour=black) + scale_colour_manual(values=c(green,red)) + guides(shape = guide_legend(Observed, override.aes=list(shape = 16))) though this is a bit of a hack. 3) legends are automatically created when you map something to an aesthetic. see ?guide and guide_legend etc. for customization. Best, Ista On Mon, Oct 13, 2014 at 4:43 PM, David Bourne da...@boomer.org wrote: I’m trying to generate a plot with a series of data points and best fit lines from two stat models. I’m generating the best-fits with another program. I have the data in a csv file as: Time,Observed,Calculated,Model 0.000,0.0,13.0810,1C 0.2500,15.,12.5298,1C 0.5000,12.,12.0018,1C 1.000,9.0,11.0117,1C 2.000,8.0,9.26969,1C 4.000,6.5,6.56882,1C 6.000,4.8,4.65489,1C 9.000,3.2,2.77680,1C 12.00,2.1,1.65641,1C 18.00,1.8,0.589422,1C 24.00,0.90,0.209736,1C 0.000,0.0,21.7130,2C 0.2500,15.,15.0512,2C 0.5000,12.,11.8203,2C 1.000,9.0,9.29374,2C 2.000,8.0,7.82242,2C 4.000,6.5,6.20213,2C 6.000,4.8,4.93346,2C 9.000,3.2,3.50010,2C 12.00,2.1,2.48310,2C 18.00,1.8,1.24979,2C 24.00,0.90,0.629039,2C I read in the data with (R 3.1.1 GUI 1.65 Mavericks build (6784)): rtest - read.csv(rtest.csv,header=TRUE) Checked with rtest Time Observed Calculated Model 1 0.00 0.0 13.0810001C 2 0.25 15.0 12.5298001C 3 0.50 12.0 12.0018001C 4 1.00 9.0 11.0117001C 5 2.00 8.0 9.2696901C 6 4.00 6.5 6.5688201C 7 6.00 4.8 4.6548901C 8 9.00 3.2 2.7768001C 9 12.00 2.1 1.6564101C 10 18.00 1.8 0.5894221C 11 24.00 0.9 0.2097361C 12 0.00 0.0 21.7130002C 13 0.25 15.0 15.0512002C 14 0.50 12.0 11.8203002C 15 1.00 9.0 9.2937402C 16 2.00 8.0 7.8224202C 17 4.00 6.5 6.2021302C 18 6.00 4.8 4.9334602C 19 9.00 3.2 3.5001002C 20 12.00 2.1 2.4831002C 21 18.00 1.8 1.2497902C 22 24.00 0.9 0.6290392C Generated the graph with: ggplot(rtest, aes(x=Time, y=Calculated,color=Model, group=Model)) + geom_line(aes(size=2)) + geom_point(aes(y=Observed, size=Observed), size=6, colour=black) + scale_colour_manual(values=c(green,red)) + labs(size=Observed”) Which resulted in the plot: http://www.boomer.org/rtest/rtest.pdf I’d like to: 1) get rid of the Observed / 2 legend(guide) 2) maybe keep the Observed and have a circle, i.e., loose the ‘2’ 3) understand how to create, format the legend This seems like a common enough problem but the online documentation, R for Dummies nor the R Graphic Cookbook seems to have an answer (from my reading). Thanks for any clues/suggestion. David __ 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-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] RAxML using R
Dear Rui, thanks for the reply. I tried it but there were serious problems with the function itself: I can't use it as is because there were different error messages according to the parameters passed to it. Such function has to be re-written on purpose or I should call the RAxML function from Linux Shell with some kind of interface. Best regards Luigi On 29/12/14 11:18, Rui Barradas wrote: Hello, You can put the code of that function in a separate file, say raxml.R and then use source(raxml.R). See the help page ?source. Hope this helps, Rui Barradas Em 28-12-2014 23:09, Luigi Marongiu escreveu: Dear all, I would like to run RAxML for phylogenetic analysis as indicated on Paradis' Analysis of phylogenetic and evolution with R (p. 158) thus I tried to use the package phyloch and its function raxml(). I am using Linux Ubuntu 14 and I was able to successfully install RAxML on my machine. However phyloch is not supported by CRAN and the package as provided by the author on http://www.christophheibl.de/Rpackages.html cannot be installed -- or at least I could not using the Software centre. I found a function on http://www.christophheibl.de/raxml.R that should be the actual script of the function raxml(). However I do not know how to implement such function. Shall I just copy it into my script? But then I will have hundreds of lines of code that will mess with my script. Can I create a kind of package that I can call from my code? Or is there another way to send the data to the linux shell and call the raxml function? Could anybody help? Thank you Luigi __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] RAxML using R
Let's step back for a moment and look at the larger picture. There are real advantages to using packages on CRAN, including convenient installation, updates, etc., so let's see if we can find a package on cran that does what we want: install.packages(sos) library(sos) findFn(raxml) This suggests that the ips package does what we want, so let's install it and test it out: install.packages(ips) library(ips) example(raxml) If for some reason this does not do what you want, and you really need to install packages outside the standard R package repositories, the devtools package will make this easier: library(devtools) nstall_url(http://www.christophheibl.de/phyloch_1.5-5.tar.gz;) library(phyloch) example(raxml) HTH, Ista On Mon, Dec 29, 2014 at 9:23 AM, Luigi Marongiu marongiu.lu...@gmail.com wrote: Dear Rui, thanks for the reply. I tried it but there were serious problems with the function itself: I can't use it as is because there were different error messages according to the parameters passed to it. Such function has to be re-written on purpose or I should call the RAxML function from Linux Shell with some kind of interface. Best regards Luigi On 29/12/14 11:18, Rui Barradas wrote: Hello, You can put the code of that function in a separate file, say raxml.R and then use source(raxml.R). See the help page ?source. Hope this helps, Rui Barradas Em 28-12-2014 23:09, Luigi Marongiu escreveu: Dear all, I would like to run RAxML for phylogenetic analysis as indicated on Paradis' Analysis of phylogenetic and evolution with R (p. 158) thus I tried to use the package phyloch and its function raxml(). I am using Linux Ubuntu 14 and I was able to successfully install RAxML on my machine. However phyloch is not supported by CRAN and the package as provided by the author on http://www.christophheibl.de/Rpackages.html cannot be installed -- or at least I could not using the Software centre. I found a function on http://www.christophheibl.de/raxml.R that should be the actual script of the function raxml(). However I do not know how to implement such function. Shall I just copy it into my script? But then I will have hundreds of lines of code that will mess with my script. Can I create a kind of package that I can call from my code? Or is there another way to send the data to the linux shell and call the raxml function? Could anybody help? Thank you Luigi __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Getting HR from Cox model in R
Comments in line
On 24/12/2014 22:09, Ruzan Udumyan wrote:
Dear Michael,
Thank you very much for your reply. The more complete information is as
follows:
I want to do a mediation analysis following the below-mentioned syntax
from:
http://www.biomedcentral.com/content/supplementary/1471-2288-14-9-s1.pdf
I did not define categorical variables as logical variables. I modelled
them as /*factor.X, factor.Xstar*/, etc. the X variable has 3 levels.
It is all sorted but I am not sure about the last bit: to return the
values. For example, I could not figure out what unname stands for, and
whether it is correct to use when variables are modelled as factor.X.
I wrote the syntax as:
TE2 = exp(sum(coef(cox)[c('factor(X)2', 'factor(Xstar)2')])) # level
2 vs level 1(ref)
TE3 = exp(sum(coef(cox)[c('factor(X)3', 'factor(Xstar)3')])) # level
3 vs level 1(ref)
DE2 = exp(unname(coef(cox)['factor(X)2']))
DE3 = exp(unname(coef(cox)['factor(X)3']))
IE2 = exp(sum(coef(cox)['factor(Xstar)2']))
IE3 = exp(sum(coef(cox)['factor(Xstar)3']))
PM2 = log(IE2) / log(TE2)
PM3 = log(IE3) / log(TE3)
Thank you very much for your help.
Wishing you happy holidays,
Ruzan
*The script from the link:*
doEffectDecomp = function(d)
{
# Step 1: Replicate exposure variable, predict mediator
d$TrialTemp = d$Trial
MOpti = glm(Opti ~ TrialTemp + Age5 + ECOG + Ascit + Comorb + Histo +
Grade, family=binomial(), data=d)
# Step 2: Replicate data with different exposures for the mediator
d1 = d2 = d
d1$Med = d1$Trial
d2$Med = !d2$Trial
newd = rbind(d1, d2)
# Step 3: Compute weights for the mediator
newd$TrialTemp = newd$Trial
w = predict(MOpti, newdata=newd, type='response')
direct = ifelse(newd$Opti, w, 1-w)
newd$TrialTemp = newd$Med
w = predict(MOpti, newdata=newd, type='response')
indirect = ifelse(newd$Opti, w, 1-w)
newd$W = indirect/direct
# Step 4: Weighted Cox Model
cox = coxph(Surv(OS, Status) ~ Trial + Med + Age5 + ECOG + Ascit +
Comorb + Histo + Grade, weight=W, data=newd)
# Return value: Estimates for total, direct, indirect effect
TE = exp(sum(coef(cox)[c('TrialTRUE', 'MedTRUE')]))
DE = exp(unname(coef(cox)['TrialTRUE']))
IE = exp(sum(coef(cox)['MedTRUE']))
PM = log(IE) / log(TE)
return(c(exp(coef(cox)), TE=TE, DE=DE, IE=IE, PM=PM))
}
On Tue, Dec 23, 2014 at 6:21 PM, Michael Dewey i...@aghmed.fsnet.co.uk
mailto:i...@aghmed.fsnet.co.uk wrote:
Inline comments
On 23/12/2014 09:42, Ruzan Udumyan wrote:
Dear All,
I am not familiar with R language well. Could you please help me
interpret
these commands?:
TE = exp(sum(coef(cox)[c('aTRUE', 'bTRUE')])) - does it
mean exp(coef(a
variable) + coef(b variable)) ?
You have not given us much to go on here.
I assume if you go
coef(cox)
you will find elements labelled aTRUE and bTRUE which implies the
existence of a logical covariate with values TRUE and FALSE.
You now tell me my assumption was wrong. Presumably you know what you
are trying to do but we do not and you are not really helping us by
giving us a load of code to read through with any details of the dataset.
The
author of the code is trying to do what you suggest.
DE = exp(unname(coef(cox)['aTRUE'])__) - what is unname for ?
?unname
Thank you very much beforehand for your help.
Wishing you happy holidays,
Ruzan
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[R] attribute and main value
Just a curiosity question:
In the documentation for the nlm procedure
a find this example of defining a function
with a gradient attribute:
---
f - function(x, a)
{
res - sum((x-a)^2)
attr(res, gradient) - 2*(x-a)
res
}
---
I get the gradient with
attr(f(3,2),gradient)
but how do I get the function value it self?
Gerrit
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Re: [R] half-logistic distribution
Therneau, Terry M., Ph.D. therneau at mayo.edu writes:
On 12/26/2014 05:00 AM, r-help-request at r-project.org wrote:
i want to analyse survival data using typeI HALF LOGISTIC
DISTRIBUTION.how can i go about it?it installed one on R in the
survival package didn't include the distribution...or i need a code to
use maximum likelihood to estimate the parameter in survival
analysis.a typical example of distribution other than that installed
in R will help.thanks
I am the author of the survival package, and had never heard of the
type I half-logistic before. New distributions can be added to
survreg() if they can be represented as location-scale families; I
don't think that your distribution can be written in that way. Look
at survival under the Task Views tab (upper left corner) of the
cran.org web page
I don't know about 'type I' but based on
http://en.wikipedia.org/wiki/Half-logistic_distribution
you could try
dhalflogist - function(x,s,log=FALSE) {
r - log(2)-log(s)-x/s-2*log(1+exp(-x/s))
if (log) r else exp(r)
}
phalflogist - function(q,s) { (1-exp(-q/s))/(1+exp(-(q/s))) }
rhalflogist - function(n,s) { abs(rlogis(n,scale=s)) }
set.seed(101)
rr - rhalflogist(1,2)
hist(rr,freq=FALSE,breaks=80,col=gray)
curve(dhalflogist(x,2),add=TRUE,col=2,lwd=2)
d - data.frame(rr)
library(bbmle)
m1 - mle2(rr~dhalflogist(s=exp(logs)),start=list(logs=0),data=d)
If you want to fit multiple groups etc., see the 'parameters'
argument of ?mle2
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Re: [R] attribute and main value
On 29/12/2014 10:32 AM, Gerrit Draisma wrote:
Just a curiosity question:
In the documentation for the nlm procedure
a find this example of defining a function
with a gradient attribute:
---
f - function(x, a)
{
res - sum((x-a)^2)
attr(res, gradient) - 2*(x-a)
res
}
---
I get the gradient with
attr(f(3,2),gradient)
but how do I get the function value it self?
value - f(3,2)
gradient - attr(value, gradient)
Duncan Murdoch
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Re: [R] attribute and main value
Thanks Duncan.
But my question was how to extract
simply the function value from value,
without the gradient attribute?
I see that things like value2 give the right answer.
I was curiosity. I found now that value[1]
gives strips the attributes from value:
--
value
[1] 1
attr(,gradient)
[1] 2
value[1]
[1] 1
--
Is that the way?
Gerrit
On 12/29/2014 05:05 PM, Duncan Murdoch wrote:
On 29/12/2014 10:32 AM, Gerrit Draisma wrote:
Just a curiosity question:
In the documentation for the nlm procedure
a find this example of defining a function
with a gradient attribute:
---
f - function(x, a)
{
res - sum((x-a)^2)
attr(res, gradient) - 2*(x-a)
res
}
---
I get the gradient with
attr(f(3,2),gradient)
but how do I get the function value it self?
value - f(3,2)
gradient - attr(value, gradient)
Duncan Murdoch
__
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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] attribute and main value
as.vector(x) will return x without any attributes and
structure(x, attrA=NULL, attrB=NULL) will return x
without the named attributes.
z - f(1:3, 4)
z
[1] 14
attr(,gradient)
[1] -6 -4 -2
as.vector(z)
[1] 14
structure(z, gradient=NULL)
[1] 14
as.vector is a generic function, so for certain classes
(e.g., factor) it may do more than just strip attributes.
Matrix dimensions are attributes and will be removed by
as.vector.
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Mon, Dec 29, 2014 at 8:17 AM, Gerrit Draisma gdrai...@xs4all.nl wrote:
Thanks Duncan.
But my question was how to extract
simply the function value from value,
without the gradient attribute?
I see that things like value2 give the right answer.
I was curiosity. I found now that value[1]
gives strips the attributes from value:
--
value
[1] 1
attr(,gradient)
[1] 2
value[1]
[1] 1
--
Is that the way?
Gerrit
On 12/29/2014 05:05 PM, Duncan Murdoch wrote:
On 29/12/2014 10:32 AM, Gerrit Draisma wrote:
Just a curiosity question:
In the documentation for the nlm procedure
a find this example of defining a function
with a gradient attribute:
---
f - function(x, a)
{
res - sum((x-a)^2)
attr(res, gradient) - 2*(x-a)
res
}
---
I get the gradient with
attr(f(3,2),gradient)
but how do I get the function value it self?
value - f(3,2)
gradient - attr(value, gradient)
Duncan Murdoch
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Re: [R] attribute and main value
Thanks Bill,
That is what I was looking for.
Gerrit
On 12/29/2014 05:53 PM, William Dunlap wrote:
as.vector(x) will return x without any attributes and
structure(x, attrA=NULL, attrB=NULL) will return x
without the named attributes.
z - f(1:3, 4)
z
[1] 14
attr(,gradient)
[1] -6 -4 -2
as.vector(z)
[1] 14
structure(z, gradient=NULL)
[1] 14
as.vector is a generic function, so for certain classes
(e.g., factor) it may do more than just strip attributes.
Matrix dimensions are attributes and will be removed by
as.vector.
Bill Dunlap
TIBCO Software
wdunlap tibco.com http://tibco.com
On Mon, Dec 29, 2014 at 8:17 AM, Gerrit Draisma gdrai...@xs4all.nl
mailto:gdrai...@xs4all.nl wrote:
Thanks Duncan.
But my question was how to extract
simply the function value from value,
without the gradient attribute?
I see that things like value2 give the right answer.
I was curiosity. I found now that value[1]
gives strips the attributes from value:
--
value
[1] 1
attr(,gradient)
[1] 2
value[1]
[1] 1
--
Is that the way?
Gerrit
On 12/29/2014 05:05 PM, Duncan Murdoch wrote:
On 29/12/2014 10:32 AM, Gerrit Draisma wrote:
Just a curiosity question:
In the documentation for the nlm procedure
a find this example of defining a function
with a gradient attribute:
---
f - function(x, a)
{
res - sum((x-a)^2)
attr(res, gradient) - 2*(x-a)
res
}
---
I get the gradient with
attr(f(3,2),gradient)
but how do I get the function value it self?
value - f(3,2)
gradient - attr(value, gradient)
Duncan Murdoch
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[R] Add labels to my geom_hlines...
Dear Users, Would you pls help me to write a proper geom_text addition to geom_hline of my ggplot2. This is d.oran Tarih EUROUSD USDJPY EUROJPY 1 2005-01-01 1.378200 1.034654 1.425960 2 2005-01-02 1.373217 1.027268 1.410662 3 2005-01-03 1.364489 1.024884 1.398443 4 2005-01-04 1.352766 1.026722 1.388914 5 2005-01-05 1.338793 1.033870 1.384138 this is d.oran.ist x_barstd_sap EUROUSD 1.3405613 0.08739358 USDJPY 0.9909699 0.13757461 EUROJPY 1.3265161 0.18982005 and both of them are (in function) data frames. Here is my ggplot and i'd like to add texts to left edge of hlines like -3sigma, -2sigma,..., 3sigma. Here is my ggplot line: ggplot(data=d.oran)+geom_line(aes(x=Tarih,y=EUROUSD))+geom_hline(data=d.oran.ist,yintercept=x_bar[1]+c(-3:3)*std_sap[1],color=c(red,green,blue,black,blue,green,red),linetype=dashed,lwd=1) Thanks in advance. Levent TERLEMEZ. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Some questions on R
To whom it may concern My name is Kenta Suzuki. Today I subscribed the R-help. I do not know about this R-help in detail but I send this e-mail since I have some trouble on programming R. When I type help() in general id dose not work properly. For instance, when I type, help(plot) in R, it shows; Error in file(out, wt) : cannot open the connection on the Internet. Beside the problem, when I try to install packages like rgl, it always shows; Warning in install.packages(rgl) : 'lib = C:/Program Files/R/R-3.1.2/library' is not writable Error in install.packages(rgl) : unable to create ‘C:\Users\U+5065U+592A\Documents/R/win-library/3.1’ In addition: Warning message: In dir.create(userdir, recursive = TRUE) : cannot create dir 'C:\Users\U+5065U+592A', reason 'Invalid argument' How can I fix these problems? I would be great if you give me the solutions. Yours Sincerely, Kenta Suzuki [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] fuction to find outlier
Hi all, I am stuck on outlier, while doing regression analysis. I done up to
modelling ,I got lm model for each y and x.
Now I want to find out outlier in that models. How do I find out outlier and
remove them.
for(i in 1:10){
t1=print(outlierTest(fitted.modely1.temp.l ,cutoff=0.05, n.max=1, order=TRUE))
print(outlierTest(fitted.modely2.avg.pcp,cutoff=0.05, n.max=1, order=TRUE))
outlierTest(fitted.modely3.bshc,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely4.bsco ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely5.gets ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely6.gimep,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely7.ts ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely8.imp ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely9.maf ,cutoff=0.05, n.max=1, order=TRUE)
i-i+1
}
fix(xsys)
[[alternative HTML version deleted]]
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[R] Difference between eigs() and eigen()
Dear R users and contributors,
I recently observed a difference between the outputs of the classic
eigen() function, and the Arnoldi variant eigs() that extracts only
the few first eigenpairs. Here is some sample code illustrating the
problem:
library(rARPACK)
library(speccalt)
set.seed(1)
# compute kernel matrix from rows of synth5
# then its Laplacian
kern - local.rbfdot(synth5)
diag(kern) - 0
deg - sapply(1:(dim(synth5)[1]), function(i) {
return(sum(kern[i,]))
})
L - diag(1/sqrt(deg)) %*% kern %*% diag(1/sqrt(deg))
eig1 - eigs(L, 6)
eig2 - eigen(L, symmetric=TRUE)
eig1$values then reads:
1.000 1.000 0.9993805 0.9992561 0.9985084 0.9975311
whereas eig2$values reads:
1.000 1.000 1.000 1.000 0.9993805 0.9992561
which is the correct result (eigenvalue 1 has multiplicity 4 in that example).
I guess there is an issue between Arnoldi methods and eigenvalues with
multiplicities greater than 1 (indeed at the end of the series the
unique eigenvals look identical), but as the problem is not documented
in the package PDF, I'm quite unclear if this is
implementation-specific or Arnoldi-general... The issue is quite
important in my case, as the associated eigenvectors then differ quite
significantly, and this impacts negatively my further operations.
I guess the next step is to dig into the mathematical literature, but
before this I wondered if someone already encountered this issue?
Any help would be appreciated,
Pierrick
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[R] Memory hungry routines
Is there any way to detect which calls are consuming memory? I run a program whose global variables take up about 50 Megabytes of memory, but when I monitor the progress of the program it seems to allocating 150 Megabytes of memory, with peaks of up to 2 Gigabytes. I know that the global variables aren't copied many times by the routines, but I suspect something weird must be happening. Alberto Monteiro PS: the lines, below, count the memory allocated to all global variables, probably it could be adapted to track the local variables: y - ls(pat=) # get all names of the variables z - rep(0, length(y)) # create array of sizes for (i in 1:length(y)) z[i] - object.size(get(y[i])) # loop: get all sizes (in bytes) of the variables # BTW, is there any way to vectorialize the above loop? xix - sort.int(z, index.return = TRUE) # sort the sizes y - y[xix$ix] # apply the sort to the variables z - z[xix$ix] # apply the sort to the sizes y - c(y, total) # add a totalizator z - c(z, sum(z)) # sum them all cbind(y, z) # ugly way to list them __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Difference between eigs() and eigen()
eigs() is from a contributed package. No idea what it is about, but my
guess is these are actually numerical differences coming from different
algorithms used to calculate the eigenvalues.
For details, please ask the author of the corresponding contributed package.
Best,
Uwe Ligges
On 29.12.2014 19:02, Pierrick Bruneau wrote:
Dear R users and contributors,
I recently observed a difference between the outputs of the classic
eigen() function, and the Arnoldi variant eigs() that extracts only
the few first eigenpairs. Here is some sample code illustrating the
problem:
library(rARPACK)
library(speccalt)
set.seed(1)
# compute kernel matrix from rows of synth5
# then its Laplacian
kern - local.rbfdot(synth5)
diag(kern) - 0
deg - sapply(1:(dim(synth5)[1]), function(i) {
return(sum(kern[i,]))
})
L - diag(1/sqrt(deg)) %*% kern %*% diag(1/sqrt(deg))
eig1 - eigs(L, 6)
eig2 - eigen(L, symmetric=TRUE)
eig1$values then reads:
1.000 1.000 0.9993805 0.9992561 0.9985084 0.9975311
whereas eig2$values reads:
1.000 1.000 1.000 1.000 0.9993805 0.9992561
which is the correct result (eigenvalue 1 has multiplicity 4 in that example).
I guess there is an issue between Arnoldi methods and eigenvalues with
multiplicities greater than 1 (indeed at the end of the series the
unique eigenvals look identical), but as the problem is not documented
in the package PDF, I'm quite unclear if this is
implementation-specific or Arnoldi-general... The issue is quite
important in my case, as the associated eigenvectors then differ quite
significantly, and this impacts negatively my further operations.
I guess the next step is to dig into the mathematical literature, but
before this I wondered if someone already encountered this issue?
Any help would be appreciated,
Pierrick
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Memory hungry routines
On 29/12/2014 1:52 PM, ALBERTO VIEIRA FERREIRA MONTEIRO wrote: Is there any way to detect which calls are consuming memory? The Rprofmem() function can do this, but you need to build R to enable it.Rprof() does a more limited version of the same thing if run with memory.profiling = TRUE. Duncan Murdoch I run a program whose global variables take up about 50 Megabytes of memory, but when I monitor the progress of the program it seems to allocating 150 Megabytes of memory, with peaks of up to 2 Gigabytes. I know that the global variables aren't copied many times by the routines, but I suspect something weird must be happening. Alberto Monteiro PS: the lines, below, count the memory allocated to all global variables, probably it could be adapted to track the local variables: y - ls(pat=) # get all names of the variables z - rep(0, length(y)) # create array of sizes for (i in 1:length(y)) z[i] - object.size(get(y[i])) # loop: get all sizes (in bytes) of the variables # BTW, is there any way to vectorialize the above loop? xix - sort.int(z, index.return = TRUE) # sort the sizes y - y[xix$ix] # apply the sort to the variables z - z[xix$ix] # apply the sort to the sizes y - c(y, total) # add a totalizator z - c(z, sum(z)) # sum them all cbind(y, z) # ugly way to list them __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] fuction to find outlier
Dear Pushpa Methekar,
This apparently uses the outlierTest() function in the car package. I'm
afraid that I find your code incomprehensible and there's not enough
information here to reproduce what you've done. You're looping over the
index i in 1:10, explicitly incrementing the loop index at the end of the
loop (which, thankfully, will have no effect), and doing the same things in
each iteration -- i.e., repeatedly executing several commands without
modification. All this seems terribly confused and not clearly related to
locating and dealing with outliers.
Furthermore, it's not clear to me that what you propose is sensible -- to
delete outliers after performing statistical tests, in each case for a
single outlier, apparently mechanically and without investigating what's
going on in each case. It would probably be a good idea to consult a
competent statistician to help you decide how to proceed.
All that said, to answer your question directly, probably the easiest way to
remove an observation from a fitted model is to use update() with the subset
argument. For example model.1 - update(model, subset = -6) would remove
case 6 from model and assign the result to model.1.
Best,
John
---
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.socsci.mcmaster.ca/jfox/
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of
Methekar, Pushpa (GE Transportation, Non-GE)
Sent: Monday, December 29, 2014 7:37 AM
To: r-help@r-project.org
Subject: [R] fuction to find outlier
Hi all, I am stuck on outlier, while doing regression analysis. I done
up to modelling ,I got lm model for each y and x.
Now I want to find out outlier in that models. How do I find out outlier
and remove them.
for(i in 1:10){
t1=print(outlierTest(fitted.modely1.temp.l ,cutoff=0.05, n.max=1,
order=TRUE))
print(outlierTest(fitted.modely2.avg.pcp,cutoff=0.05, n.max=1,
order=TRUE))
outlierTest(fitted.modely3.bshc,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely4.bsco ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely5.gets ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely6.gimep,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely7.ts ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely8.imp ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely9.maf ,cutoff=0.05, n.max=1, order=TRUE)
i-i+1
}
fix(xsys)
[[alternative HTML version deleted]]
__
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guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: [R] Installation Help
I guess I skipped an option during installation. I removed the program now. Do u think I should choose something else than administrator? From: emorway [via R] [mailto:ml-node+s789695n4701164...@n4.nabble.com] Sent: Monday, December 29, 2014 12:13 AM To: Bros Subject: Re: Installation Help do you have write permissions to your c: drive? If you're on a machine that requires administrative privileges, it might only allow you to write to c:/users. _ If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/Installation-Help-tp4701143p4701164.html To unsubscribe from Installation Help, click here http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by _codenode=4701143code=aHVzZXlpbi5rYXJhZ3VsQGdtYWlsLmNvbXw0NzAxMTQzfDEyNDE4 NzE1MjE= . http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=macro_vieweri d=instant_html%21nabble%3Aemail.namlbase=nabble.naml.namespaces.BasicNamesp ace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNa mespacebreadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails% 21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml NAML --- This email has been checked for viruses by Avast antivirus software. -- View this message in context: http://r.789695.n4.nabble.com/Installation-Help-tp4701143p4701190.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Installation Help
Let windows prompt you for your password when it is needed... don't run anything as Administrator directly unless you are prepared to fix the problems that occur. --- Jeff NewmillerThe . . Go Live... DCN:jdnew...@dcn.davis.ca.usBasics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --- Sent from my phone. Please excuse my brevity. On December 29, 2014 11:46:35 AM PST, Bros huseyin.kara...@gmail.com wrote: I guess I skipped an option during installation. I removed the program now. Do u think I should choose something else than administrator? From: emorway [via R] [mailto:ml-node+s789695n4701164...@n4.nabble.com] Sent: Monday, December 29, 2014 12:13 AM To: Bros Subject: Re: Installation Help do you have write permissions to your c: drive? If you're on a machine that requires administrative privileges, it might only allow you to write to c:/users. _ If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/Installation-Help-tp4701143p4701164.html To unsubscribe from Installation Help, click here http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by _codenode=4701143code=aHVzZXlpbi5rYXJhZ3VsQGdtYWlsLmNvbXw0NzAxMTQzfDEyNDE4 NzE1MjE= . http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=macro_vieweri d=instant_html%21nabble%3Aemail.namlbase=nabble.naml.namespaces.BasicNamesp ace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNa mespacebreadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails% 21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml NAML --- This email has been checked for viruses by Avast antivirus software. -- View this message in context: http://r.789695.n4.nabble.com/Installation-Help-tp4701143p4701190.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] fuction to find outlier
Pushpa,
To extend John Fox's answer a little.
Look at the outliers dataset in the TeachingDemos package, see
?outliers and run the examples on that help page. Then ask yourself if
you are comfortable with the automatic outlier removal shown in the example.
On Mon, Dec 29, 2014 at 12:39 PM, John Fox j...@mcmaster.ca wrote:
Dear Pushpa Methekar,
This apparently uses the outlierTest() function in the car package. I'm
afraid that I find your code incomprehensible and there's not enough
information here to reproduce what you've done. You're looping over the
index i in 1:10, explicitly incrementing the loop index at the end of the
loop (which, thankfully, will have no effect), and doing the same things in
each iteration -- i.e., repeatedly executing several commands without
modification. All this seems terribly confused and not clearly related to
locating and dealing with outliers.
Furthermore, it's not clear to me that what you propose is sensible -- to
delete outliers after performing statistical tests, in each case for a
single outlier, apparently mechanically and without investigating what's
going on in each case. It would probably be a good idea to consult a
competent statistician to help you decide how to proceed.
All that said, to answer your question directly, probably the easiest way
to
remove an observation from a fitted model is to use update() with the
subset
argument. For example model.1 - update(model, subset = -6) would remove
case 6 from model and assign the result to model.1.
Best,
John
---
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.socsci.mcmaster.ca/jfox/
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of
Methekar, Pushpa (GE Transportation, Non-GE)
Sent: Monday, December 29, 2014 7:37 AM
To: r-help@r-project.org
Subject: [R] fuction to find outlier
Hi all, I am stuck on outlier, while doing regression analysis. I done
up to modelling ,I got lm model for each y and x.
Now I want to find out outlier in that models. How do I find out outlier
and remove them.
for(i in 1:10){
t1=print(outlierTest(fitted.modely1.temp.l ,cutoff=0.05, n.max=1,
order=TRUE))
print(outlierTest(fitted.modely2.avg.pcp,cutoff=0.05, n.max=1,
order=TRUE))
outlierTest(fitted.modely3.bshc,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely4.bsco ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely5.gets ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely6.gimep,cutoff=0.05, n.max=1, order=TRUE )
outlierTest(fitted.modely7.ts ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely8.imp ,cutoff=0.05, n.max=1, order=TRUE)
outlierTest(fitted.modely9.maf ,cutoff=0.05, n.max=1, order=TRUE)
i-i+1
}
fix(xsys)
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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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.
--
Gregory (Greg) L. Snow Ph.D.
538...@gmail.com
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Re: [R] Memory hungry routines
You might find the advice at http://adv-r.had.co.nz/memory.html helpful. Hadley On Tue, Dec 30, 2014 at 7:52 AM, ALBERTO VIEIRA FERREIRA MONTEIRO albm...@centroin.com.br wrote: Is there any way to detect which calls are consuming memory? I run a program whose global variables take up about 50 Megabytes of memory, but when I monitor the progress of the program it seems to allocating 150 Megabytes of memory, with peaks of up to 2 Gigabytes. I know that the global variables aren't copied many times by the routines, but I suspect something weird must be happening. Alberto Monteiro PS: the lines, below, count the memory allocated to all global variables, probably it could be adapted to track the local variables: y - ls(pat=) # get all names of the variables z - rep(0, length(y)) # create array of sizes for (i in 1:length(y)) z[i] - object.size(get(y[i])) # loop: get all sizes (in bytes) of the variables # BTW, is there any way to vectorialize the above loop? xix - sort.int(z, index.return = TRUE) # sort the sizes y - y[xix$ix] # apply the sort to the variables z - z[xix$ix] # apply the sort to the sizes y - c(y, total) # add a totalizator z - c(z, sum(z)) # sum them all cbind(y, z) # ugly way to list them __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- http://had.co.nz/ __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Memory hungry routines
On Mon, Dec 29, 2014 at 10:52 AM, ALBERTO VIEIRA FERREIRA MONTEIRO albm...@centroin.com.br wrote: Is there any way to detect which calls are consuming memory? I run a program whose global variables take up about 50 Megabytes of memory, but when I monitor the progress of the program it seems to allocating 150 Megabytes of memory, with peaks of up to 2 Gigabytes. I know that the global variables aren't copied many times by the routines, but I suspect something weird must be happening. Alberto Monteiro PS: the lines, below, count the memory allocated to all global variables, probably it could be adapted to track the local variables: y - ls(pat=) # get all names of the variables z - rep(0, length(y)) # create array of sizes for (i in 1:length(y)) z[i] - object.size(get(y[i])) # loop: get all sizes (in bytes) of the variables # BTW, is there any way to vectorialize the above loop? xix - sort.int(z, index.return = TRUE) # sort the sizes y - y[xix$ix] # apply the sort to the variables z - z[xix$ix] # apply the sort to the sizes y - c(y, total) # add a totalizator z - c(z, sum(z)) # sum them all cbind(y, z) # ugly way to list them Duncan already suggested Rprofmem(). For a neat interface to that, see also lineprof package. Common memory hogs are cbind(), rbind() and other ways of incrementally building up objects. These can often be avoided by pre-allocating the final object up front and populating it as you go. Another source of unnecessary memory duplication is coercion of data types, e.g. allocating an integer matrix but populating it with doubles. A related mistake is to use matrix(nrow, ncol) for allocate matrices that will hold numeric values. That is actually doing matrix(NA, nrow, ncol), which becomes a *logical* matrix, which will be coerced (involving copying and large memory allocation) the first thing as soon as it get's populated with a numeric value. One should have used matrix(NA_real_, nrow, ncol) here. For listing objects, their sizes and more, you can use ll() in the R.oo package which returns a data.frame, e.g. example(iris) a - 1:1e6 R.oo::ll() member data.class dimension objectSize 1 anumeric 100440 2 dni3 list 3600 3 ii data.frame c(150,5) 7088 4 iris data.frame c(150,5) 7088 R.oo::ll(sortBy=objectSize) member data.class dimension objectSize 2 dni3 list 3600 3 ii data.frame c(150,5) 7088 4 iris data.frame c(150,5) 7088 1 anumeric 100440 tbl - R.oo::ll() tbl - tbl[order(tbl$objectSize, decreasing=TRUE),] tbl member data.class dimension objectSize 1 anumeric 100440 3 ii data.frame c(150,5) 7088 4 iris data.frame c(150,5) 7088 5 objs data.framec(4,4) 2760 2 dni3 list 3600 sum(tbl$objectSize) [1] 4017576 /Henrik __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
