similar to: SR-IOV vs nPAR

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

Dear Nouveau developers, I have a Dell precision M3520 laptop and I have installed fedora 26 but the nouveau driver does not seem to recognise my graphic card NVIDIA Quadro M620. Indeed I can't see it in the llist of the supported card. Do you have any idea whether it will become available and when? Thank you for your help. All the best, Patrick

Dear Trevor, I've noticed a discrepancy in the degrees of freedom reported by gam() from the gam package in R vs. gam() in S-PLUS. The nonparametric df differ by 1; otherwise (except for things that depend upon the df), the output is the same: --------- snip ------------ *** From R (gam version 0.93): > mod.gam <- gam(prestige ~ lo(income, span=.6), data=Prestige) >

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

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 <<-

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 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

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

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

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,

I've been looking for real-world examples of this for a long time -- mainly because I'm trying to decide whether to restrict my build-options to using only components which are all compatible with IOV. I'm thinking that the constraint would be worth it, if IOV could provide a significant performance benefit for non-network peripherals such as storage, video processing, etc. (compared

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

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

Greetings, Following patches add SR-IOV capability support to the Linux kernel. With these patches, people can turn a PCI device with the capability into multiple ones from software perspective. [PATCH 1/4 v2] PCI: introduce new base functions [PATCH 2/4 v2] PCI: support ARI capability [PATCH 3/4 v2] PCI: support SR-IOV capability [PATCH 4/4 v2] PCI: document the change Thanks to Randy Dunlap

Greetings, Patches to support Single Root I/O Virtualization (SR-IOV) capability are available for the Linux 2.6 development tree. KVM and Xen supports will come soon! --- Single Root I/O Virtualization (SR-IOV) capability defined by PCI-SIG is intended to enable multiple system software to share PCI hardware resources. PCI device that supports this capability can be extended to one Physical

Hi, We recently purchased some HP Gen8 servers with Broadcom BCM57810 NIC controllers. I am running CentOS 6.2 on these servers configured as KVM hosts. Has anyone gotten the Broadcom chips to run with SR-IOV on CentOS 6.2? Any clues on how to get it working? I see from the Broadcom glossy marketing docs that this chip supports SR-IOV, but I cannot find a procedure for configuring it. The