similar to: Discrepancy between gam from gam package and gam in S-PLUS

Core developers, I couldn't find any simple way to send a vector of colors to apply to each terminal in plot.dendrogram() or plot.hclust()---I asked R-help about it a few weeks ago and didn't get any response---so I hacked that functionality into the plot.dendrgram code (see below for hacked function plus examples).... Is there any chance this functionality could be added to the

I am running 1.3.1 on a Windows (NT 4.0) machine. I've fit a nonlinear model intended to predict crop yield from nutrient information, and want to use the profile function. If I type say, profile(simparj.fm) I get the following error message: "Error in prof$getProfile(): number of iterations exceeded maximum of 5.25515e-308" I used the profiler function to profile simparj,fm step

Hi, (Already asked this on the intel wired forum: http://communities.intel.com/message/185413#185413 but some of those questiones are XEN specific, which might be why theres no response ;-) I have doubts if SR-IOV will provide the required features compared to nPAR. SR-IOV seems more flexible, but I''m not sure if performance and features are the same as nPAR. Servers will be Dell M620

Dear Prof. Thomas Yee I$B!G(Bm very interested in your R program VGAM. I tried below your data: # Nonparametric proportional odds model data(pneumo)pneumo = transform(pneumo, let=log(exposure.time))vgam(cbind(normal,mild,severe) ~ s(let), cumulative(par=TRUE), pneumo) However, the results by Version of VGAM are different; ----------The result by Version 0.7-7

The function 'regsubsets' appears to calculate a BIC value that is different from that returned by the function 'BIC'. The latter is explained in the documentation, but I can't find an expression for the statistic returned by 'regsubsets'. Incidentally, both of these differ from the BIC that is given in Ramsey and Schafer's, The Statistical Sleuth. I assume

I apologize if this has been asked before but I've look for a long time with no success. My problem is that I want to annotate a plot with an expression that combines parameter names with fitted values for from 1 to n parameters depending on the problem - something like R = 16.1, P[m] = 4.51, k[a] = 7.23, alpha[r] = .01 ... with the [] values as subscripts. I thought that because the number

Dear list, I was trying to use the R API for optimization method "Simulated annealing" void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex); but I encountered the following problem: The implementation of the function samin (as seen in src/main/optim.c) passes its void * argument "ex" into the function

Dear list, I am brand new to R and using Dalgaard's (2002) book Introductory Statistics with R (thus, some of my terminology may be incorrect). I am fitting regression models and I want to use Hurvich and Tsai's AICC statistic to examine my regression models. This penalty can be expressed as: 2*npar * (n/(n-npar-1)). While you can obtain AIC, BIC, and logLik, I want to impose the AICC

Dear Dr Skaug and R users, just discovered glmm.admb in R, and it seems a very useful tool. However, I ran into a problem when I compare two models: m1<-glmm.admb(survival~light*species*damage, random=~1, group="table", data=bm, family="binomial", link="logit") m1.1<-glmm.admb(survival~(light+species+damage)^2, random=~1, group="table", data=bm,

Dear R Users on a self-written function for calculating maximum likelihood probability (plz check function code at the bottom of this message), one value, wden, suddenly jump to zero. detail info as following: w[11]=2.14 lnw =2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182 0.178 0.179... w[11]=2.14 lnw=2.37 2.90 3.76 ... regw =1.96 1.77 1.82 .... wden=0.182

Hi All, I would like to compute the simple finite-difference approximation to the gradient of a scalar function of a large number of variables (on the order of 1000). Although a one-time computation using the following function grad() is fast and simple enough, the overhead for repeated evaluation of gradient in iterative schemes is quite significant. I was wondering whether there are

In trying to streamline various optimization functions, I would like to have a scratch pad of working data that is shared across a number of functions. These can be called from different levels within some wrapper functions for maximum likelihood and other such computations. I'm sure there are other applications that could benefit from this. Below are two approaches. One uses the <<-

Hi: In R, how do I display some intermediate results calculated in a "for" loop within a function? For example, in the attached code, how do I get it to print the intermediate variable "mh.new" for each simulation, when I call the function "MHsim.ind"? thanks for any help, Ravi. #################################################################### MHsim.ind

Hi all, I would like to know how the BIC criterion is calculated for models estimated using gls( ) function. I read in Pinheiro & Bates (2000) p84 that BIC = -2logL + npar*log(N) (for the ML method), or BIC = -2logLR + npar*log(N-p) (for the REML method) but when I use any of these formulae I don't obtain the result given by R. Thanks in advance for any help. Eve CORDA Office national

Hi, I am trying to carry out a multinomial regression using the cumlogit link function. I have tried using the VGAM package, and have gotten some results... fit1 <- vgam(Y ~ X1 + X2 + X3 + X4, cumulative(link=logit,intercept.apply=FALSE,parallel=TRUE), data = data1 ) The problem arrises when I try to get the information out of the fitted object. I can

Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1)

I'm posting this for two reasons: one is to see if anyone has a better way of solving the problem or suggestions for improving my existing approach; and the other is to show what I'm currently doing in case anyone else might find it useful. The background is that I've been using Sweave for several years now to produce class notes, and I sometimes include quite a lot of raw R input and

Having worked with optim() and related programs for years, it surprised me that I haven't noticed this before, but optim() is inconsistent in how it deals with bounds constraints specified at infinity. Here's an example: # optim-glitch-Ex.R x0<-c(1,2,3,4) fnt <- function(x, fscale=10){ yy <- length(x):1 val <- sum((yy*x)^2)*fscale } grt <- function(x, fscale=10){ nn

Previously, I've posted queries about this, and thanks to postings and messages in response have recently had some success, to the extent that there is now a package called nlmrt on the R-forge project https://r-forge.r-project.org/R/?group_id=395 for solving nonlinear least squares problems that include small or zero residual problems via a Marquardt method using a call that mirrors the nls()

Hey, R users I do not know how to describe my question. I am a new user for R and write the following?code for a dynamic labor economics?model and use OPTIM to get optimizations and parameter values. the following code does not work due to the?equation: ?? wden[,i]<-dnorm((1-regw[,i])/w[5])/w[5] where w[5]?is one of the parameters (together with vector a, b and other elements in vector