similar to: weighted nls and confidence intervals

dear list members, I apologize in advance for posting a second time, but probably after one week chances are, the first try went down the sink.. my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as expected, as is detailed in my original post below. any remarks appreciated. greetings

dear list members, my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as (I) expected. I'm posting this here since: (A) the problem might suggest a modification to the `m' component in the return argument of `nls' (making this post formally OK for this list) and (B) I got no

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

Dear all, I found really difficult with the time series questions, please help me with this monthly airline series! I have run the following r code, and there is an error appeared at the end. The data files was enclosed in the email. I'm sorry the errors message appeared in chinese, but it says "plot.xy(xy.coords(x, y), type = type, ...) : errors in argument has more than 3

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

Suppose I have the following data: tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 = sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace = TRUE)) I can run the following double loop and yield what I want in the end (rr1) as: library(statmod) Q <- 2 b <- runif(3) qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1) rr1 <- matrix(0,

Dear R-Users, I have a problem on extracting T-Stat and P-Value. I have written R-code below library("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <-

Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09

Hello, I've a weired problem with a data frame. Basically it should be just one column with specific names coming from a data file (the file contains 2 rows, one should be the for the rownames of the data frame the other contains numeric values). > df.rr <- read.table("RR_anova.txt", header=T, comment.char="", row.names=1) > df.rr[c(1,2,3),] [1] 1.11e-16 1.11e-16

Greetings everyone, I have the following problem (illustrating R-code at bottom of mail): Given a training sample with binary outcomes (-1/+1), I train a linear Support Vector Machine to separate them. Afterwards, I compute the weight vector w in the usual way, and obtain the fitted values as w'x + b > 0 ==> yfitted = 1, otherwise -1. However, upon verifying with the

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

Dear r-users, I would like to convert my Matlab code to R-code, however it dies not work as expected.  Hope somebody can help me to match Matlab and r codes. R code: rr <- function(r,cxn) { tol <- 1E-4; for(i in 1:n) { t1 <- (1+(i-1)*r)*log((1+(i-1)*r)) t2 <- (i-1)*(1-r)*log(1-r) rri <- ((t1+t2)/i*log(i))-cxn rr <- rri > tol } round(rr,4) } rr1 <- rr(0.5,0.0242) ; rr1

Dear useRs, I'm trying to simply fill in the area under a curve using RGL. Here' the set up: x <- c(0.75,75.75,150.75,225.75,300.75,375.75,450.75,525.75,600.75,675.75, 0.5,50.5,100.5,150.5,200.5,250.5,300.5,350.5,400.5,450.5, 0.25,25.25,50.25,75.25,100.25,125.25,150.25,175.25,200.25,225.25) y <- c(0.05,4.91,9.78,14.64,19.51,24.38,29.24,34.11,38.97,43.84,

I am trying to do a MLE fit of the weibull to some data, which I attach. fitweibull<-function() { rt<-scan("r/rt/data2/triam1.dat") rt<-sort(rt) plot(rt,ppoints(rt)) a<-9 b<-.27 fn<-function(p) -sum( log(dweibull(rt,p[1],p[2])) ) cat("starting -log like=",fn(c(a,b)),"\n") out<-nlm(fn,p=c(a,b), hessian=TRUE)

Hi R-users: [R 2.2 on OSX 10.4.3] I have a (sparse) vegetation data frame with 500 rows (sampling units) and 177 columns (plant species) where the data represent % cover. I need to summarize the cover data by returning the names of the most dominant and the second most dominant species per plot. I reduced the data frame to omit cover below 5%; this is what it looks like stacked. I have

I have the following code which produces the output below it clus1 <- svydesign(ids = ~schid, data = lower_dat) items <- as.formula(paste(" ~ ", paste(lset, collapse= "+"))) rr1 <- svymean(items, clus1, deff='replace', na.rm=TRUE) > rr1 mean SE DEff W525209 0.719748 0.015606 2.4932 W525223 0.508228 0.027570 6.2802 W525035 0.827202

Hello, I'm new to this and am trying to teach myself some R by plotting biological data. The growth curve in question is supposed to be fitted to the Verhulst equation, which may be transcribed as follows: f(x)=a/(1+((a-0.008)/0.008)*exp(-(b*x))) - for a known population density (0.008) at t(0). I am trying to rework the example from "An Introduction to R" (p. 72) for my case and

Change the assembly code to use only relative references of symbols for the kernel to be PIE compatible. Position Independent Executable (PIE) support will allow to extended the KASLR randomization range below the -2G memory limit. Signed-off-by: Thomas Garnier <thgarnie at google.com> --- arch/x86/crypto/aes-x86_64-asm_64.S | 45 ++++++++----- arch/x86/crypto/aesni-intel_asm.S

Hi. This patchset is preparation patches for ia64/pv_ops support. They are almost trivial and mainly make kernel paravirtualization friendly. thanks, Diffstat: arch/ia64/kernel/irq_ia64.c | 1 - include/asm-ia64/intrinsics.h | 11 +++++++++++ include/asm-ia64/mmu_context.h | 6 +----- include/asm-ia64/smp.h | 2 ++ include/asm-ia64/system.h | 10 ++++++++-- 5 files