similar to: questions about performing Robust multiple regression using bootstrap

Dear list, I am slightly confused about how I can do the following in R. I want to perform robust multiple regression. I?ve used the Boot function in CAR package to find confidence intervals and standard errors. Inadition to these, I want to find the robust estimates for the F test and r-square. Finally, I would like to know the significance levels of bootstrap results. Below I explain

Hi all, I am trying to use type message with sink, like this: sink("all.Rout", type="message") 1+3 sink() readLines(con = "all.Rout") So to get the following output: > 1+3 [1] 4 Obviously this doesn't work. I tried some variations (based on the explanations in the help) but am missing something on how to make it work. Any suggestions? (p.s: I need

You might also want to recruit on the R news group... r-help at stat.math.ethz.ch. -Greg > -----Original Message----- > From: Jan Ivanouw [mailto:ivanouw at post8.tele.dk] > Sent: Wednesday, January 16, 2002 8:28 AM > To: s-news at lists.biostat.wustl.edu > Subject: [S] Study group on bootstrap > > > Hi > I am looking for participants for an e-mail

Residuals are stored as a numeric vector. The R software comes with a document "Introduction to R" that discusses basic math functions and logical operators that can create logical vectors: abs( stdresiduals ) > 2.5 It also discusses indexing using logical vectors: stdresiduals[ abs( stdresiduals ) > 2.5 ] Note that in most cases it is worth going the extra step of making your

I'm trying to adapt some S-Plus scripts to run in R (1.2.2, Windows). In one of these scripts, I've bootstrapped the prediction success rate under the discriminant function (lda). The bootstrap() functions are proprietary to S-Plus and there aren't exact equivalents in R. The closest is Canty's library boot based on the Davidson and Hinkley book. Unfortunately, I

I am trying to perform goodness of fit test using R. I am using this website http://wiener.math.csi.cuny.edu/Statistics/R/simpleR/stat013.html for help. However, I am unable to carry out the test successfully. My code follows. It is taken from the website just mentioned. freq=c(22,21,22,27,22,36) # frequencies obtained after rolling the dice 150 times. prob=c(1,1,1,1,1,1)/6 # specify expected

Dear users, I'm trying to implement the nonparametric "two-stage" bootstrap (Davison and Hinkley 1997, pag 100-102) in R. As far as I understood, 'bootcov' is the most appropriate method to implement NONPARAMETRIC bootstrap in R when you have clustered data (?). I read the 'bootcov' manual but I still have a few questions: 1 - When the variable 'cluster' is

Akhilesh, I don't know if I am adding signal or noise here, but I installed R 3.5.0 yesterday and installed data.table just fine, but I have Rtools installed. "Building packages from source" may sound intimidating, but Rtools is really easy to install and once installed, it does all the work for you. Thanks, Roger -----Original Message----- From: R-help [mailto:r-help-bounces

Good Morning: I posted an initial question a few days ago and I received some good advice from two R experts. I have re-examined the Davison-Hinkley text paying close attention to the examples of the boot() and boot.ci() in that text and the single example of a similar process in the MASS book (not the MASS package manual as I initially misunderstood). I think I understand how the stratified

On 26/04/2018 1:54 PM, Akhilesh Singh wrote: > My thanks to Dr. John Fox and Dr. Duncan Murdoch. But, I have upgraded > all my R-3.4.3 libraries to R-3.5.0, and I have not backed-up copies of > old version. So, I would give a try each to the solutions suggested by > John Fox and Dengan Murdoch. Here is some unsolicited advice: I would strongly recommend that you make it a higher

Dear Akhilesh, I hope that it's clear that the Windows binary I provided for the data.table package is a temporary solution, and that the maintainer should fix the package so that it passes its own tests. You should be careful using the package in its current state. Best, John > -----Original Message----- > From: Akhilesh Singh [mailto:akhileshsingh.igkv at gmail.com] > Sent:

My thanks to Dr. John Fox and Dr. Duncan Murdoch. But, I have upgraded all my R-3.4.3 libraries to R-3.5.0, and I have not backed-up copies of old version. So, I would give a try each to the solutions suggested by John Fox and Dengan Murdoch. With regards, Dr. A.K. Singh On Thu 26 Apr, 2018, 9:44 PM Duncan Murdoch, <murdoch.duncan at gmail.com> wrote: > On 26/04/2018 10:33 AM, Fox,

If you're installing packages to the default location in your home account and you didn't remove those library folders, you still have you R 3.4 package installs there, e.g. > dir(dirname(.libPaths()[1]), full.names = TRUE) [1] "/home/hb/R/x86_64-pc-linux-gnu-library/3.4" [2] "/home/hb/R/x86_64-pc-linux-gnu-library/3.5" [3]

Hi For first question, maybe I am completely wrong but cor(swiss[,-1], swiss[,1]) should give you what you want in one step. Second question Without an example it is hard to say but maybe aggregate is the way forward. > aggregate(iris[,1:4], list(iris$Species), function (x) c(mean=mean(x), sd=sd(x))) Group.1 Sepal.Length.mean Sepal.Length.sd Sepal.Width.mean Sepal.Width.sd 1

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

You are right. I do take backups. But, this time I was too sure that nothing will go wrong. But, this was over-confidence. I need to take more care in future. Thanks anyway. With regards, Dr. A.K. Singh On Thu 26 Apr, 2018, 11:49 PM Duncan Murdoch, <murdoch.duncan at gmail.com> wrote: > On 26/04/2018 1:54 PM, Akhilesh Singh wrote: > > My thanks to Dr. John Fox and Dr. Duncan

Hmm, looks like that thread has more noise than signal... AFAICT, data.table currently fails selftests i 3.5.0 on all platforms on the CRAN builders, so RTools issues are only incidental and it would be better to fix data.table in the sources. From the looks of it, I wouldn't be surprised if the root cause is the changes to POSIXlt methods in 3.5.0, but I haven't actually been digging

After reading more, I understand I didn't formulate my last question correctly, so please allow me to rephrase: What I am looking for is a way to save the R console session output. That is, a command that would combine the results of using: ?sink # And ?savehistory My motivation for this is that doing it will allow someone who is a blind user of R to be able to easily export his results to

Dear A.K. Singh, As you discovered, the data.table package has an error under R 3.5.0 that prevents CRAN from distributing a Windows binary for the package. The reason that you weren't able to install the package from source is apparently that you haven't installed the R package-building tools for Windows. See <https://cran.r-project.org/bin/windows/Rtools/>. Because a number of

You might find this discussion useful, too: https://github.com/Rdatatable/data.table/issues/2797 On 04/26/2018 11:01 PM, Henrik Bengtsson wrote: > If you're installing packages to the default location in your home > account and you didn't remove those library folders, you still have > you R 3.4 package installs there, e.g. > >> dir(dirname(.libPaths()[1]), full.names =