similar to: Linear Models with positive coefficients?

Hi, recently I saw but did not pay too much attention to a question that concerned regression with positive coefficients. In Splus, thereis the nnls() function that can be used if I am not wrong, but what about R ? Now I have the same problem: doing a logistic regression under constraint that coefs are non negative. What can I do with R? is there a (weighted) nnls() counterpart available? Thanks

Hi, using R64.1 the summary function for simple statistics of a vector gives inaccurate results for the maximum. Example: summary(c(123456,1,2,3)) gives : Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 1.75 2.50 30870.00 30870.00 123500.00 The Max value ist wrong in a mathematical sense. I've tried with S-Plus, it gives the same result, but this seems

Hi, i want to use R as a filter in a Unix-like way. So i want to start it with producerTool | R --no-save --no-restore --slave < Rscript | anotherConsumerUnixTool inside the Script i put the results to stdout, works fine, but how to read in from stdin? Its obvious that i have to source the Rscript in a different way. Any Hints? My second question is, if there is a possibility to use

Hi, if make and gmake are different on any machine (for example Solaris with gnu make as gmake), then compiling fails with gmake. A small transcript follows. If i make a link from gmake to make as under linux, then it works. This problem exists in all versions of R. g77 -O2 -fPIC -c sslvrg.f -o sslvrg.o g77 -O2 -fPIC -c stxwx.f -o stxwx.o ../../../../bin/R SHLIB -o modreg.so bsplvd.o bvalue.o

I have downloaded rw0632 (Windows Version) from the CRAN at ETH Zuerich. Im working with NT4.0 Service Pack 4. I can start without any problems the GUI program Rgui.exe. But by using Rterm.exe (batch mode or with ESS respectivelly) I always get an error "the instruction at "0x10018ee8" referenced memory at ""......." Any ideas what is wrong? Best Regards Markus

There is a documentation of janka hardness data in MASS, but no data. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

Hi all, I am trying to do an inversion of electromagnetic data with non-negative least squares method (Tikhonov regularisation) and have got it programmed in S-Plus. However I am trying to move all my scripts from S-Plus to R. Is there an equivalent to nnls.fit in R? I think this can be done with pcls? Right? S-Plus script: A, L and data are matrices, lambda is a vector of possible lambda

Dear R users, I am currently attempting to fit logistic regression models in R, where the slopes should be restricted to positive values. Although I am aware of the package nnls (which does the trick for linear regression models), I did not find any solution for logistic regression. If there is any package available for this purpose, I would be interested to know them. Alternatively, I realize

I'm trying to get significance of coefficients as for lm() but I news help. Inviato da iPad [[alternative HTML version deleted]]

I'd like to find the dataset files cloud.data, janka.data, house.data ... used in Appendix C of "Notes on R" by Bill Venables and Dave Smith. Can someone give me a pointer to a download site, please? With thanks, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk> Date: 17-Jun-98

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is

hi, I am new to the list, and wonder where can I find the data sets used in the exercises at the end of the "Notes on R" ... thanks, -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body",

This seems to be a recurrent topic, but I don't remember hearing a definitive answer. I also apologies for cross-posting. Say I have a numerical response variable and a bunch of multi-level factors I want to use for modeling. I don't expect factor interaction to be important so there will be no interactions in the model. All this would be a perfect job for ANOVA except for one additional

I wonder whether R has methods for constrained fitting of linear models. I am trying fm<-lm(y~x+I(x^2), data=dat) which most of the time gives indeed the coefficients of an inverted parabola. I know in advance that it has to be an inverted parabola with the maximum constrained to positive (or zero) values of x. The help pages for lm do not contain any info on constrained fitting. Does anyone

I'm hoping that someone with deeper insight into S4 than I, that is to say virtually everyone reading this list, could help resolve the following problem in SparseM. We have setGeneric("backsolve", function(r, x, k = NULL, upper.tri = NULL, transpose = NULL, twice = TRUE, ...) standardGeneric("backsolve"), useAsDefault= function(r, x,

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Dear R users & experts, I'd like to create a model using lm (or glm) under some constraints of how coefficients for each component could look like (sort of a range of coefficients that should be allowed). So let's go for an example : model=lm(age ~ eyecolor + height, data=inputdata) So let's suppose that R pops out a model with positive estimates for the coefficients eyecolor

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

I'm using the hclust method from the package mva (pre-compiled-version on Win NT) and would like to cut the tree in pieces. Have someone implemented the function cutree (as in S-PLUS) in R? Many thanks Markus Huerzeler -- Dr. Markus Huerzeler Phone: +41 / 61 / 686 98 83 AICOS Technologies AG Fax: +41 / 61 / 686 98 88 Efringerstrasse 32 mailto:mhuerzeler at

A new package 'nnls' is now available on CRAN. The package provides an R interface to the Lawson-Hanson NNLS algorithm for non-negative least squares that solves the least squares problem A x = b with the constraint x >= 0. The Lawson-Hanson NNLS algorithm was published in Lawson CL, Hanson RJ (1974). Solving Least Squares Problems. Prentice Hall, Englewood Cliffs, NJ. Lawson CL,