similar to: Wierd nlm behaviour in 2.10.1 and 2.12.0 [Sec=Unclassified]

It would be really nice if nlm took a set of "..." optional arguments that were passed through to the objective function. This level of hacking is probably slightly beyond me: is there a reason it would be technically difficult/inefficient? (I have a vague memory that it used to work this way either in S-PLUS or in some previous version of R, but I could easily be wrong.) Here's

I am trying to view the source code of the function nlm in the stats package of R 2.4.1. I downloaded the source from CRAN and opened nlm.R, and it calls a .Internal function: .Internal(nlm(function(x) f(x, ...), p, hessian, typsize, fscale, msg, ndigit, gradtol, stepmax, steptol, iterlim)) This is the same thing I saw when entering the function name at the R command

Hi everyone. I have a problem that I have been unable to determine either the best way to proceed and why the methods I'm trying to use sometimes fail. I'm using the pf() function in an optimization function to find a noncentrality parameter that leads to a specific value at a specified quantile. My goal is to have a general function that returns the noncentrality parameter that

Hello. I found what if you have a something like this: autocreate = Trash autocreate2 = Sent autocreate3 = Spam *Then I delete autocreate2 string* and... autocreate3 will not work. If I add autocreate2 - all work well. Yes it is seems to be right what they all must go after each otherm but if i have a 20 or 30 autocreate folders and i want to delete autocreate2 for example - all other from

Hello R Community, I've been run the following codes. However, I've been getting an unusual segfault that I'm unable to trace its origin. Please give me a light to decipher the "caught segfault" Thanks for you attention. Bernardo. > options(STERM='iESS', editor='emacsclient') > rm(list = ls()) > > source("fgenIGLD.R") #RNG

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Buenas tardes, tengo 2 dfs: Dieta de (108x11) y Abund de (591x108). Necesito multiplicar cada columna de la 1ª (108 elementos) por cada fila de la 2ª (108 elementos) y crear una nueva df con las sumas de esas multiplicaciones. He hecho esto, pero no sale y creo que está lejos de estar bien: Res <- matrix(nrow=nrow(Abund),ncol=ncol(Dieta)) Res <- as.data.frame(Res) for(i in

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Does not look like the demangle test worked anyway :( And 'make check' :- $ make check make[1]: Entering directory `/usr/build/llvm-gcc/gcc' (rootme=`${PWDCMD-pwd}`; export rootme; \ srcdir=`cd /usr/cfrontend/src/gcc; ${PWDCMD-pwd}` ; export srcdir ; \ cd testsuite; \ EXPECT=expect ; export EXPECT ; \ if [ -f ${rootme}/../expect/expect ] ; then \ TCL_LIBRARY=`cd

Hi, Got everything built but am now experiencing new errors on making the symbol tables and on 'make check'. Here's the errors I am experiencing :- Aaron Gray at AMD-LAPTOP-1 /usr/llvm-gcc/lib $ ls gcc libdummy.a libiberty.a libstdc++.a libsupc++.la libc.a libgcc.a libm.a libstdc++.la libtrace.a libcrtend.a libgcsemispace.a

Dear all, I have run the confint() function according to below and I get the following error message: > confint(stepAIC.glm.spe.var.konn2.abund, level=0.95) Waiting for profiling to be done... Error: missing value where logical needed In addition: Warning message: NaNs produced in: sqrt((fm$deviance - OriginalDeviance)/DispersionParameter) or > confint(stepAIC.glm.spe.var.konn2.abund,

Hello all, I'm a beginner in R working on a script that will produce a set of models (linear, polynomial and logistic) for each location in a dataset. However, the self-starting logistic model often fails - if this happens I would like to just skip to the next iteration of the loop using tryCatch. I've looked at a few examples and read the help file, but didn't understand tryCatch

I wonder if someone could explain why, when I perform confirmatory factor-analysis model using polychoric correlations why I do not get an estimated confidence interval for the RMSEA. My experience with these type models is that I would obtain a confidence interval estimate. I did not get any warning messages with the output. RESULTS: Model Chisquare = 1374 Df = 185 Pr(>Chisq) = 0

Hello everybody, In am trying to analyse a BACI experiment and I really want to do it with R (which I find really exciting). So, before moving on I though it would be a good idea to repeat some known experiments which are quite similar to my own. I tried to reproduce 2 published examples but without much success. The first one in particular is a published dataset analysed with SAS by

It is a trivial problem, but in the book I couln`t figure out how to put different colours at different regression lines plot(bif,abund,type="n", xlab= "number_bifurcations", ylab="abundances") sbif<-split(bif,stage) sabund<-split(abund,stage) points(sabund[[2]],sbif[[2]],pch=16, col="red") for(i in 1:2) abline(lm(sabund[[i]]~sbif[[i]])) Thanks in

Dear R- Community, to learn i reanalysed some data provided and analysed by Zuur et. al. in their book "Mixed effect models and Extensions in Ecology with R". When i run the last command i get a warning message i dont understand. Loyn<- read.table(file = "loyn.txt",header = TRUE) Loyn$L.AREA<- log10(Loyn$AREA) fGRAZE <-factor(Loyn$GRAZE) M0<- lm(ABUND~ L.AREA

Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara

I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Hi, I try to make an analyses to discover what is the time that an area begin to have spacial autocorrelation. And after, what is the number of individuals responsible for this autocorrelation. The main idea is to discover if exist a contamination of a quadrat from others quadrats and how is the population needed to make this contamination. This is very common to use automata to simulate

Dear, I would like to post the following question to the r-help on Nabble (thanks in advance for the attention, Gustavo Vieira): Hi there. I have a data set on hands with 5,220 cases and I'd like to automate some procedures (but I have almost no programming knowledge). The data has some continuous variables that are grouped by 2 others: the name of species and the locality where they were