[RsR] 'ROptRegTS' load error

Matthias Kohl M@tth|@@@Koh| @end|ng |rom @t@m@t@@de
Sat Nov 29 19:06:33 CET 2008


to make the use of ROptRegTS and RobRex a little bit simpler at least 
with respect to installation, I introduced the package ROptEstOld on 
R-Forge. So, for the moment one should install ROptEstOld, ROptRegTS and 
RobRex via R-Forge by using (after they are built on R-Forge, which 
should be tomorrow)

install.packages("RobRex", repos="http://R-Forge.R-project.org")

Best,
Matthias


Johannes Graumann wrote:
> Thanks for your help. It will take until next week to look into this
> and get back.
>
> Joh
>
> On Fri, Nov 28, 2008 at 11:34 AM, Matthias Kohl
> <Matthias.Kohl using stamats.de> wrote:
>   
>> Dear Joh,
>>
>> in case of our packages ROptEst and RobLox we recently implemented the
>> functions roptest and roblox, which enable the computation of (in our sense)
>> optimally robust estimators via one single function.
>>
>> In case of our regression packages we do not have such functions so far.
>> However, we plan to implement these functions in the near future also using
>> the formula interface of R. For the time being you have to do the necessary
>> steps by hand.
>>
>> After the installation of these packages, you should find a subfolder
>> "scripts" in the package folders of ROptEst, ROptRegTS and RobRex, as well
>> as a folder "tests" in case of package RobLox. These folders contain several
>> examples how to use these packages.
>>
>> Let us for instance consider a regression of the form y ~ x.
>>
>> Some points you should think about are:
>> What error distribution do you assume? If normal, go for package RobRex. If
>> something different, go for package ROptRegTS.
>> May x and y both be contaminated or only y?
>>
>> Two examples using RobRex ...
>>
>> ###############################################################################
>> ## Example 1
>> ###############################################################################
>> require(MASS)
>> require(robustbase)
>> library(ISwR)
>> data(thuesen)
>> attach(thuesen)
>>
>> ## LS-estimator
>> fit.LS <- lm(short.velocity ~ blood.glucose)
>> ## M-estimator
>> fit.M <- rlm(short.velocity ~ blood.glucose)
>> ## MM-estimator
>> fit.MM <- lmrob(short.velocity ~ blood.glucose)
>> ## LTS-estimator
>> fit.LTS <- ltsReg(short.velocity ~ blood.glucose)
>>
>> ## AL-estimators
>> ## regressor distribution: design measure
>> require(RobRex)
>> K <- DiscreteMVDistribution(cbind(1,blood.glucose))
>> ## ALc-estimator: conditional neighborhood; i.e., only y is contaminated
>> ## about 10 sec. on my system
>> system.time(IC1 <- rgsOptIC.ALc(r = 0.5, K = K, theta = fit.M$coeff, scale =
>> fit.M$s))
>> ALc1 <- oneStepEstimator(cbind(1,as.matrix(thuesen)), IC1, c(fit.M$coeff,
>> fit.M$s))
>> ## AL-estimator: unconditional neighborhood; i.e., x and y are contaminated
>> ## about 85 sec. on my system
>> system.time(IC2 <- rgsOptIC.AL(r = 0.5, K = K, theta = fit.MM$coeff, scale =
>> fit.MM$s))
>> AL1 <- oneStepEstimator(cbind(1,as.matrix(thuesen)), IC2, c(fit.MM$coeff,
>> fit.MM$s))
>>
>> ## Plot
>> require(RColorBrewer)
>> myCol <- brewer.pal(6, "Set1")
>> plot(short.velocity ~ blood.glucose, ylab = "fasting blood glucose
>> [mmol/l]",
>>   xlab = "mean circumferential shortening velocity [%/s]",
>>   main = "Ventricular shortening velocity", pch = 20)
>> abline(fit.LS, lwd = 2, col = myCol[1])
>> abline(fit.M, lwd = 2, col = myCol[2])
>> abline(fit.MM, lwd = 2, col = myCol[3])
>> abline(fit.LTS, lwd = 2, col = myCol[4])
>> lines(c(1, c(blood.glucose,25)), ALc1[1] + ALc1[2]*c(1,c(blood.glucose,25)),
>>    col = myCol[5], lwd = 2)
>> lines(c(1, c(blood.glucose,25)), AL1[1] + AL1[2]*c(1,c(blood.glucose,25)),
>>    col = myCol[6], lwd = 2)
>> legend("topleft", legend = c("LS", "M", "MM", "LTS", "ALc", "AL"),
>>     fill = myCol, ncol = 2)
>> detach(thuesen)
>>
>> ###############################################################################
>> ## Example 2
>> ###############################################################################
>> data(phones)
>> attach(phones)
>>
>> ## LS estimator
>> fit2.LS <- lm(calls ~ year)
>> ## M estimator
>> fit2.M <- rlm(calls ~ year, maxit = 50)
>> ## MM estimator
>> fit2.MM <- lmrob(calls ~ year)
>> ## LTS estimator
>> fit2.LTS <- ltsReg(calls ~ year)
>>
>> ## AL estimators
>> ## regressor distribution: design measure
>> K <- DiscreteMVDistribution(cbind(1,year))
>> ## ALc estimator: only y contaminated
>> system.time(IC1 <- rgsOptIC.ALc(r = 0.5, K = K, theta = fit2.M$coeff, scale
>> = fit2.M$s))
>> ## takes about 9 sec. on my system
>> ALc2 <- oneStepEstimator(cbind(1,year,calls), IC1, c(fit2.M$coeff,
>> fit2.M$s))
>> ## AL estimator: x and y contaminated
>> system.time(IC2 <- rgsOptIC.AL(r = 0.5, K = K, theta = fit2.MM$coeff, scale
>> = fit2.MM$s))
>> ## takes about 80 sec. on my system
>> AL2 <- oneStepEstimator(cbind(1,year,calls), IC2, c(fit2.MM$coeff,
>> fit2.MM$s))
>>
>> ## Plot
>> plot(calls ~ year, ylab = "phone calls [Mio.]", xlab = "year",
>>   main = "Belgium Phone Calls 1950-1973", pch = 20)
>> abline(fit2.LS, lwd = 2, col = myCol[1])
>> abline(fit2.M, lwd = 2, col = myCol[2])
>> abline(fit2.MM, lwd = 2, col = myCol[3])
>> abline(fit2.LTS, lwd = 2, col = myCol[4])
>> lines(c(1, c(year,75)), ALc2[1] + ALc2[2]*c(1,c(year,75)), col = myCol[5],
>> lwd = 2)
>> lines(c(1, c(year,75)), AL2[1] + AL2[2]*c(1,c(year,75)), col = myCol[6], lwd
>> = 2)
>> legend("topleft", legend = c("LS", "M", "MM", "LTS", "ALc", "AL"),
>>     fill = myCol, ncol = 2)
>>
>> #########################################################
>>
>> Like in case of RobLox there will be changes to RobRex in the near future
>> which will clearly speed up these computations ...
>>
>> By the way, I made some further tests ...
>> Using ROptEst version 0.5 (from CRAN archive) and the recent versions of
>> distr (2.0.3), distrEx (2.0.3) and RandVar (0.6.6), the recent versions
>> (0.6.1, available from RForge) of ROptRegTS and RobRex install and check
>> without problems on my system (R version 2.8.0 Patched (2008-11-26 r47022),
>> i686-pc-linux-gnu). I'm just about to submit the new versions of our
>> distr-family as well as the new versions of RandVar, RobAStBase, ROptEst and
>> RobLox to CRAN. Because of the incompatibility of ROptRegTS and RobRex with
>> the new implementation one has to download the current versions of ROptRegTS
>> and RobRex from R-Forge.
>>
>> Best,
>> Matthias
>>
>> Johannes Graumann wrote:
>>     
>>> Yes, fresh R session (see below). Whether it's working? I was looking for
>>> a function straight forward to use - along the lines of "roblox" - but
>>> failed. Can you give a hint on how to actually set up something mimicking
>>> library(robustbase)
>>> (regression <- lmrob(
>>>  y~x
>>> ))
>>>
>>> I have had very good results using "roblox" in the past and was interested
>>> in trying the regression based on the same methodology, but being a user and
>>> no specialist the package has me intimidated.
>>>
>>> Joh
>>>
>>>
>>>
>>>       
>>>> sessionInfo()
>>>>
>>>>         
>>> R version 2.8.0 (2008-10-20) x86_64-pc-linux-gnu
>>> locale:
>>> LC_CTYPE=en_US.UTF-8; <CUT>
>>>
>>> attached base packages:
>>>  [1] stats4    tcltk     tools     stats     graphics  grDevices utils
>>>   [8] datasets  methods   base
>>> other attached packages:
>>>  [1] ROptRegTS_0.6.0        distrMod_2.0.2         MASS_7.2-44
>>>  [4] ROptEst_0.5.0          RandVar_0.6.5          distrEx_2.0.2
>>>  [7] evd_2.2-4              distr_2.0.2            SweaveListingUtils_0.1
>>> [10] sfsmisc_1.0-6          startupmsg_0.5.2       robustbase_0.4-3
>>>  [13] RColorBrewer_1.0-2     geneplotter_1.20.0     annotate_1.20.1
>>> [16] xtable_1.5-4           lattice_0.17-17        MaxQuantUtils_1.17
>>>  [19] tkrplot_0.0-18         TeachingDemos_2.3      plotrix_2.5
>>> [22] gsubfn_0.3-7           proto_0.3-8            AnnotationDbi_1.3.12
>>>  [25] RSQLite_0.7-1          DBI_0.2-4              Biobase_2.1.7
>>> [28] rkward_0.4.9
>>> loaded via a namespace (and not attached):
>>> [1] grid_2.8.0         KernSmooth_2.22-22 RobAStBase_0.1.2
>>> On Wednesday 26 November 2008 19:32:47 Matthias Kohl wrote:
>>>
>>>       
>>>> Dear Joh,
>>>>
>>>> was this a fresh R session?
>>>>
>>>> I just removed version 0.6 of package ROptEst and let the rest of the
>>>> packages unchanged. Then, installed version 0.5 of ROptEst and after
>>>> that installed version 0.5 of package ROptRegTS (from CRAN). Despite
>>>> some warnings and notes during installation, all seems to work fine ...
>>>>
>>>> Does ROptRegTS work on your system?
>>>>
>>>> Best,
>>>> Matthias
>>>>
>>>> Johannes Graumann wrote:
>>>>
>>>>         
>>>>> ROptEst 0.5! Not ROptRegTS! Sorry.
>>>>>
>>>>> Joh
>>>>>
>>>>> Johannes Graumann wrote:
>>>>>
>>>>>           
>>>>>> Hello Matthias,
>>>>>>
>>>>>> ROptRegTS 0.5 installs fine on top of the most recent rest of the
>>>>>> bundle
>>>>>> from R-Forge. When loading it I get the warnings below. Whether the
>>>>>> functionality is impeded will be figured out later today.
>>>>>>
>>>>>> Thanks for your help!
>>>>>>
>>>>>> Joh
>>>>>>
>>>>>>
>>>>>>             
>>>>>>> warnings()
>>>>>>>
>>>>>>>               
>>>>>> Warning messages:
>>>>>> 1: In fun(...) ... : no valid postscript previewer found; consider
>>>>>> setting options("eps_view"=  "....")        yourself
>>>>>> 2: Multiple methods tables found for 'optIC'
>>>>>> 3: Multiple methods tables found for 'neighbor<-'
>>>>>> 4: Multiple methods tables found for 'CallL2Fam<-'
>>>>>> 5: Multiple methods tables found for 'generateIC'
>>>>>> 6: Multiple methods tables found for 'checkIC'
>>>>>> 7: Multiple methods tables found for 'clip'
>>>>>> 8: Multiple methods tables found for 'clip<-'
>>>>>> 9: Multiple methods tables found for 'cent'
>>>>>> 10: Multiple methods tables found for 'cent<-'
>>>>>> 11: Multiple methods tables found for 'stand'
>>>>>> 12: Multiple methods tables found for 'stand<-'
>>>>>> 13: Multiple methods tables found for 'lowerCase'
>>>>>> 14: Multiple methods tables found for 'lowerCase<-'
>>>>>> 15: Multiple methods tables found for 'neighborRadius'
>>>>>> 16: Multiple methods tables found for 'neighborRadius<-'
>>>>>> 17: Multiple methods tables found for 'clipLo'
>>>>>> 18: Multiple methods tables found for 'clipLo<-'
>>>>>> 19: Multiple methods tables found for 'clipUp'
>>>>>> 20: Multiple methods tables found for 'clipUp<-'
>>>>>> 21: Multiple methods tables found for 'L2deriv'
>>>>>> 22: Multiple methods tables found for 'FisherInfo'
>>>>>> 23: Multiple methods tables found for 'checkL2deriv'
>>>>>>
>>>>>> Matthias Kohl wrote:
>>>>>>
>>>>>>             
>>>>>>> Dear Joh,
>>>>>>>
>>>>>>> we are currently restructuring and extending our packages.
>>>>>>>
>>>>>>> Some parts of the package ROptEst (version 0.5) are now included in
>>>>>>> the
>>>>>>> new packages distrMod and RobAStBase. Unfortunatelly, the structure of
>>>>>>> the packages ROptRegTS and RobRex has not been updated yet. Hence, if
>>>>>>> you want to use package ROptRegTS you have to use the old version
>>>>>>> (i.e., version 0.5) of ROptEst from the archive
>>>>>>> (http://cran.at.r-project.org/src/contrib/Archive/ROptEst).
>>>>>>>
>>>>>>> These old versions of the packages ROptEst and ROptRegTS should work
>>>>>>> with R 2.8.0 and the recent versions of the packages distr, distrEx
>>>>>>> and
>>>>>>> RandVar. I will check this later today and will then give you more
>>>>>>> details on this.
>>>>>>>
>>>>>>> Best
>>>>>>> Matthias
>>>>>>>
>>>>>>> Johannes Graumann wrote:
>>>>>>>
>>>>>>>               
>>>>>>>> Hello,
>>>>>>>>
>>>>>>>> Trying to say "library(ROptRegTS)", I get the error below. Can anyone
>>>>>>>> help me pinpoint what's wrong?
>>>>>>>>
>>>>>>>> Thanks, Joh
>>>>>>>>
>>>>>>>> Error in loadNamespace(package, c(which.lib.loc, lib.loc),
>>>>>>>> keep.source
>>>>>>>> = keep.source) :
>>>>>>>>  in 'ROptRegTS' methods for export not found: neighbor, bound, Curve,
>>>>>>>>  CallL2Fam
>>>>>>>> In addition: Warning message:
>>>>>>>> In fun(...) : no valid postscript previewer found; consider setting
>>>>>>>>  options("eps_view"=  "....")      yourself
>>>>>>>> Error: package/namespace load failed for 'ROptRegTS'
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> R-SIG-Robust using r-project.org mailing list
>>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
>>>>>>>>
>>>>>>>>                 
>>>>> _______________________________________________
>>>>> R-SIG-Robust using r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
>>>>>
>>>>>           
>>>       
>> --
>> Dr. Matthias Kohl
>> www.stamats.de
>>
>>
>>     

-- 
Dr. Matthias Kohl
www.stamats.de




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