similar to: Question re estimating SE for interquantile regression coefficients

Dear All, Just when I thought I had the plague of gfortran-9 under control, I made the tactical error of allowing my mac mini to ?upgrade? to macOS 10.14.6 which apparently also upgraded Xcode to 10.3. In consequence I?m having difficulty building my packages. The current symptom is: /usr/local/clang7/bin/clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG

Yes, this did the trick, thanks very much! I?m cc?ing r-devel just for the record. Roger Koenker r.koenker at ucl.ac.uk<mailto:r.koenker at ucl.ac.uk> Department of Economics, UCL London WC1H 0AX. On Aug 7, 2019, at 4:55 PM, Steven Dirkse <sdirkse at gams.com<mailto:sdirkse at gams.com>> wrote: Roger, Updating Xcode has the unfortunate side effect of wiping out the std

Hi, I'm wondering if it is possible to install a package for R 2.0 on R 1.9.1 on Mac OS X? I'm getting this error which seems to be known issue: library("quantreg") Error in firstlib(which.lib.loc, package) : couldn't find function "lazyLoad" In addition: Warning message: package quantreg was built under R version 2.0.1 Error in

Hello all, I have a question: I am using the interquantile method to spot outliers & it gives me values of say 234 & -120 or for the higher & lower benchmarks. I don't have any issues w/ the higher end. However I don't have any negative values. My lowest possible value is 0. Should I consider 0 as an outlier? Thanks ahead for your thoughts -- View this message in

Does any have installation instructions for this? When I run install.packages('quantreg') I get: gcc -std=gnu99 -shared -o quantreg.so akj.o boot.o brute.o chlfct.o cholesky.o combos.o crq.o crqfnb.o dsel05.o etime.o extract.o idmin.o iswap.o kuantile.o mcmb.o penalty.o powell.o rls.o rq0.o rq1.o rqbr.o rqfn.o rqfnb.o rqfnc.o sparskit2.o srqfn.o srqfnc.o srtpai.o -llapack -lblas

Hi Paul, We've been through this process ourselves for the Revolution R Open project. There are a number of pitfalls to avoid, but you can take a look at how we achieved it in the build scripts at: https://github.com/RevolutionAnalytics/RRO There are also some very useful notes in the R Installation guide: https://cran.r-project.org/doc/manuals/r-release/R-admin.html#BLAS Most packages do

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

Hi, Given a data frame: df1: application id (appid), project id (pid), person months (pm), function points (fp) How do I produce linear modelling results at the appid level. That is, I would like to find the coefficent and intercept for the formula "pm ~ fp" for each application. Thanks

Hi, I would like to compute the inverse FFT of the MDF coefficients. I have noticed that some coefficients are obviously missing since I do not see the mirror effect, neither find the purely real frequency 0 coefficent that have to be found for a "real" signal. I guess the frequency 0 coefficient is 0 (average energy 0), but how should I do the mirroring and how many coefficients should

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and

The help page and vignette for summary.rq(quantreg) mention that there are three different bootstrap methods available for the se="bootstrap" argument, but I can't figure out how to select a particular method. For example, if I want to use the "xy-pair bootstrap" how do I indicate this in summary.rq? Tom -- View this message in context:

Hi all, I am trying to teach myself to use and program R (How else do you do it? lol) Anyway, I wrote a piece of code to compute coefficent alpha for a scale. As I am neither a statistician nor a programmer, I wanted people's feedback. The code appears to work (Win 95 with R 1.0.1) and I have verified my result with SPSS and it was correct (much to my astonishment!). Nonetheless, I am

Mich besch?ftig folgende Fragestellung. Ich kenne die Verteilung (lognormal) zus?tzlich weiss ich das 99%, das 90% und das 1% Quantil. Gibt es in R eine M?glichkeit die Lognormalverteilung zu finden, das heisst den korrespondierenden logmean und logsd? Vielen Dank f?r ihre Hilfe Gruss Yvonne

I'm using the arima function to estimate coefficients and also using predict.Arima to forecast. This works nicely and I can see that the results are the same as using SAS's proc arima. I can also take the coefficent estimates for a simple model like ARIMA(2,1,0) and manually compute the forecast. The results agree to 5 or 6 decimal places. I can do this for models with and without

Dear list, I've tried several times to wrap my head around the Design library, without much success. It does some really nice things, but I'm often uncomfortable because I don't understand exactly what it's doing. Anyway, one thing I really like is the latex.ols() function, which converts an R linear model formula to a LaTeX equation. So, I started writing a latex.lm() function

Why are my coefficients getting appended with a 1? It borks a match I do later against the original list that doesn't have the random 1 added to the end. > linearModel[[1]] Call: lm(formula = modelSource ~ +UNITBUILD + UNITDB + ITBUILD + ITDB + UATBUILD + UATDB + HOGANCODE + RCF + ReleaseST1 + ReleaseST2 + ReleaseBLA + Small.Bank.Acquisitions + HLY.NewYear + HLY.MLK + HLY.PRES +

Hi, I created an rpart object and pruned the tree using 1-SE rule. I used 10-fold cross validation while creating the tree. Then, I extracted the cross-validated predictions for my data points using xpred.rpart and obtained some statistics like precision, recall, overall error rate, etc. However, these values change each time I run xpred.rpart because of the random shuffling going on before

I would like to estimate bivariate VAR(X) models where I don't know the optimal lag length X and would also like to use F-tests to determine the granger causality of each of the variables. I'm aware of Achim's econometric packages description but I was wondering if someone could recommend a specific R econometrics package that does this. If it is recommended to use the sort of ideas

I?ve a data set and i want fit a kernel density estimate to the data. but using the k-nearest neighbour method. How i do this with R. thanks -- bertola at fastmail.fm --

Hi there, I'm working with clustered data sets and trying to calculate log-likelihood (and/or AIC, AICc) for my models. In using the gee and geese packages one gets Wald test output; but apparently there is no no applicable method for "logLik" (log-likelihood)calculation. Is anyone aware of a way to calculate log-likelihood for GEE models? Thanks for the help, Bruce