similar to: nonlinear constrained optimization

Can I say that RPART is a modified algo of CART and PARTY a modified of CHAID? Thanks. ---- Chua Siang Li Consultant - Operations Research Acceval Pte Ltd Tel: 6297 8740 Email: siang.li.chua at acceval-intl.com Website: www.acceval-intl.com This message and any attachments (the "message"...{{dropped:12}}

Hello there. I have fitted a rpart model. > rpartModel <- rpart(y~., data=data.frame(y=y,x=x),method="class", ....) and can use rpart$where to find out the terminal nodes that each observations belongs. Now, I have a set of new data and used predict.rpart which seems to give only the predicted value with no information similar to rpart$where. May I know how

Hello there. I uses the following codes for the purpose of variable selection. > lmModel <- lm(y~.,data.frame(y=y, x=x)) > step <- stepAIC(lmModel, direction="both") > step$anova Stepwise Model Path Analysis of Deviance Table Initial Model: y ~ x.Market.Price + x.Quantity + x.Country + x.Incoterm + x.Channel + x.PaymentTerm Final

Hi. I uses rpart to build a regression tree. Y is continuous. Now, I try to predict on a new set of data. In the new set of data, one of my x (call Incoterm, a factor) has a new level. I wonder why the error below appears as the guide says "For factor predictors, if an observation contains a level not used to grow the tree, it is left at the deepest possible node and

Dear experts, I'm attempting to solve a constrained optimization problem using the Rdonlp2 package. I created a Lagrange function (L=f(x)-lambda(g(x)-c)), where x is a vector of 16 parameters. This is what I'm using as objective function in the code below. In addition, I set bounds on these parameters (par.u and par.l). When I run the code, I get the error message shown below. Any idea

Dear R Users, I am looking for some guidance on setting up an optimisation in R with non-linear constraints. Here is my simple problem: - I have a function h(inputs) whose value I would like to maximise - the 'inputs' are subject to lower and upper bounds - however, I have some further constraints: I would like to constrain the values for two other separate function f(inputs) and

Previous versions have this question have partially bounced. I apologize if parts of this are showing up multiple times on the list. Another try ... There was at one time an R package called Rdonlp2 for solving constrained nonlinear programming problems. Both the objective function and the constraints could be nonlinear in the decision variables. The package is no longer in the CRAN list.

Hello there. Is there any function in R that can do cluster on a set of data that has both categorical and numerical variables? thanks. siangli

Dear R-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar

Dear Sir, I installed successfully using the install.packages("Rdonlp2", repos=" http://R-Forge.R-project.org"). I tried on the example which is defined on the Rdonlp help file ,p <- c(-10,10)par.l <- c(0,0); par.u <- c(100,100)lin.u <- 1; lin.l <- 1A <- t(c(1,1))fn <- function(x){ x[1]^2+x[2]^2}ret <- donlp2(p, fn, par.lower=par.l, par.upper=par.u, A=A,

Dear R-helpers. Would appreciate if someone can explain the warning messages below, after auto.arima. I couldn't find any clue in the archived help. Also, how do I retrieve the AICs of each tried model in auto.arima? The purposes are (1) to output to a text file, and (2) to find the 2nd best model by finding 2nd lowest AIC instead of eyeballing thru the value at the console

# Hi all, # consider the following code (please, run it: # it's fully working and requires just few minutes # to finish): require(CreditMetrics) require(clusterGeneration) install.packages("Rdonlp2", repos= c("http://R-Forge.R-project.org", getOption("repos"))) install.packages("Rsolnp2", repos= c("http://R-Forge.R-project.org",

Hi R, A quick question.... How can I optimize the objective function constrained to quadratic constraints? Which function of R is useful for quadratic constraints? Many Thanks, Shubha This e-mail may contain confidential and/or privileged i...{{dropped:13}}

Helo there. I am using kmeans of base package to cluster my customers. As the results of kmeans is dependent on the initial centroid, may I know: 1) how can we specify the centroid in the R function? (I don't want random starting pt) 2) how to determine the optimal (if not, a good) centroid to start with? (I am not after the fixed seed solution as it only ensure that the

Ryuichi Tamura wrote: Please can you put your package on the CRAN server ? Many thanks Diethelm Wuertz > Hello R-list, > > I have released an update version (0.3-1) of Rdonlp2. > Some (fatal) bugs which may kill interpreter should be fixed. > > In addition, user-visible changes are: > * *.mes, *.pro files are not created if name=NULL(this is default) in donlp2(). > * use

There have been several messages on R-devel mentioning the interior point optimization software Ipopt, https://projects.coin-op/Ipopt/. This C++ library is released under a license called the Common Public License. I have two questions that readers of R-devel may be able to answer. 1) Would creating an Ipopt interface for R be duplicating existing efforts? That is, has someone already done so

Hi Guys, Recently I wrote a package for dealing with Markov Switching Regressions in R and it is included in the Rmetrics project. https://r-forge.r-project.org/projects/rmetrics/ Everything works fine when I use it in computer. But, the package depends on the use of optimization functions from the package Rdonlp2, which is not available on CRAN. So, if I have Rdonlp2 in my laptop (or any

Hello to everyone! I use .Call to call a C function without arguments wich calls a fortran optimization package. My C function uses others C and Fortran functions and it works fine when I call it from a main() in a C program. But when I call it from R with .Call("name_of_the_c_function"), R gives me some weird output. This weird output is a worng answer to my optimization problem

Thanks Dragos, I assume I will be setting those parameters during initialization of encoder right? Question is, if connection gets too lossy, how will opus adapt to it? Can it automatically shift bitrate down to minimize impact? Mark from IRC suggests that the app has to be aware of the losses and change it on the fly. Has anybody on the list tried this? Kelvin Chua On Wed, Mar 4, 2015 at 5:53

Dear all, I'm wondering if there are some ongoing projects for interior point methods in R (e.g. linking ipopt from Coin written in C++ to R) and for automatic differentiation in R (e.g. linking openAD available in C++ and Fortran) ? Many thanks, David [[alternative HTML version deleted]]