similar to: Windows crash in confint() with nls fit (PR#8428)

All - I have data: TL age 388 4 418 4 438 4 428 5 539 10 432 4 444 7 421 4 438 4 419 4 463 6 423 4 ... [truncated] and I'm trying to fit a simple Von Bertalanffy growth curve with program: #Creates a Von Bertalanffy growth model VonB=nls(TL~Linf*(1-exp(-k*(age-t0))), data=box5.4, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) #Scatterplot of the data plot(TL~age, data=box5.4,

Hello all, I'm trying to use confint from the MASS package to compute confidence intervals for an nls object. When I plot the results, however, they don't make sense - lines cross over the fitted model or just don't match the data. Code is : Thanks for help dat<-data.frame(a,b) with(dat, plot(a,b)) model<-(nls(b~(1/exp(a*x))*n, data=dat, start=list(x=.001,n=20),

A non-text attachment was scrubbed... Name: not available Type: text/enriched Size: 3706 bytes Desc: not available Url : https://stat.ethz.ch/pipermail/r-help/attachments/20010913/b4135232/attachment.bin

Dear colleagues, I have used statistical software "R" in academic research, and I find very interesting. So now I have started my studies at advanced level. I have conducted several statistical models of the type "*nls*". In respect of procedures for models of the type nls, I'd like you answer only one question that I have had doubts, related to the formula

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

Dear R-helpers, I am using confint() within a function, and I want to turn off the message it prints: x <- rnorm(100) y <- x^1.1+rnorm(100) nlsfit <- nls(y ~ g0*x^g1, start=list(g0=1,g1=1)) > confint(nlsfit) Waiting for profiling to be done... 2.5% 97.5% g0 0.4484198 1.143761 g1 1.0380479 2.370057 I cannot find any way to turn off 'Waiting for. .." I tried

Hi all, I've got a list (Reg3Lst) with 1000 nls regression results in it. I'd like to get the confidence interval of the parameters obtained with the nonlinear regressions. Thus I've used this: for (i in 1:1000) { foo<-list(foo,confint(Reg3Lst[[i]])) } For some regressions the confidence interval is not estimated because of a singular gradient result. Then it

Dear R users, I?m trying to optimize simultaneously two binomials inequalities (used in acceptance sampling) which are nonlinear solution, so there is no simple direct solution. Please, let me explain shortly the the problem and the question as following. The objective is to obtain the smallest value of 'n' (sample size) satisfying both inequalities: (1-alpha) <= pbinom(c, n, p1)

I need confint() for glm() to supress the messages "Waiting for profiling to be done..." because they mess up the caching mechanism of pgfSweave (see https://github.com/cameronbracken/pgfSweave/issues/40). I have read the help page of confint(), but I do not know how to get the help page for the glm() version, if any such help page exists. Is there a general way of turning of output

Hi, I have a glm gives summary as follows, Estimate Std. Error z value Pr(>|z|) (Intercept) -2.03693352 1.449574526 -1.405194 0.159963578 A 0.01093048 0.006446256 1.695633 0.089955471 N 0.41060119 0.224860819 1.826024 0.067846690 S -0.20651005 0.067698863 -3.050421 0.002285206 then I use confint(k.glm)

Hello all - This code will run, but it bogs down my computer when I run it for finer and finer time increments and more generations. I was wondering if there is a better way to write my loops so that this wouldn't happen. Thanks! -Tyler ################# # Bifurcation diagram # Using Braaksma system of equations # We have however used a Fourier analysis # to get a forcing function

Hello, I am analyzing some climate time series data using the Mann Kendall package and was wondering if there was a way to calculate the trend using Sen's nonparametric estimator slope in R? Thank you in advance, Barry _________________________ Barry Baker, Ph.D. Global Climate Change Initiative The Nature Conservancy 2424 Spruce St., Suite 100 Boulder, CO 80302 Tel: (303)-541-0322 Fax:

It seems that confint.default returns an empty data.frame for objects of class mlm. For example: ``` nobs <- 20 set.seed(1234) # some fake data datf <- data.frame(x1=rnorm(nobs),x2=runif(nobs),y1=rnorm(nobs),y2=rnorm(nobs)) fitm <- lm(cbind(y1,y2) ~ x1 + x2,data=datf) confint(fitm) # returns: 2.5 % 97.5 % ``` I have seen proposed workarounds on stackoverflow and elsewhere, but

I can't get confint.glm to work from within a function. Consider the following (using R 1.9.1, Windows 2000): # FIRST: SOMETHING THAT WORKS FROM A COMMAND PROMPT DF <- data.frame(y=.1, N=100) (fit <- glm(y~1, family=binomial, data=DF, weights=DF[,"N"])) Call: glm(formula = y ~ 1, family = binomial, data = DF, weights = DF[, "N"]) Coefficients:

? Tue, 21 May 2024 08:00:11 +0000 Dario Strbenac via R-devel <r-devel at r-project.org> ?????: > Would a less resource-intensive value, such as 1, be a safer default > CPU value for confint? Which confint() method do you have in mind? There is at least four of them by default in R, and many additional classes could make use of stats:::confint.default by implementing vcov(). >

Hi, I am currently doing logistic regression analyses and I am trying to get confidence intervals for my partial logistic regression coefficients. Supposing I am right in assuming that the formula to estimate a 95% CI for a log odds coefficient is the following: log odds - 1.96*SE to log odds + 1.96*SE then I am not getting the right CI. For instance, this is a summary of my model:

Full_Name: Christian Lajaunie Version: 2.5.1 OS: Fedora fc6 Submission from: (NULL) (193.251.63.39) confint() does not use the appropriate variance term when the design matrix contains a zero column (which of course should not happen). Example: A 10x2 matrix with trivial column 1: > junk <- data.frame(x=rep(0,10), u=factor(sample(c("Y", "N"), 10, replace=T))) The

I have the following binary data set: Sex Response 0 1 0 159 162 1 4 37 My commands library(MASS) sib.glm=glm(sib~sex,family=binomial,data=sib.data) summary(sib.glm) The coefficients in the output are Estimate Std. Error z value Pr(>|z|) (Intercept) -3.6826 0.5062 -7.274 3.48e-13

Hello, Would a less resource-intensive value, such as 1, be a safer default CPU value for confint? I noticed excessive CPU usage on a I.T. administrator-managed server which was being three-quarters used by another staff member when the confidence interval calculation in an R Markdown document suddenly changed from two seconds to ninety seconds because of competition for CPUs between users. Also,

Dear R developers, I noticed a bug in the stats4 package, specifically in the confint method applied to ?mle? objects. In particular, when some ?fixed? parameters define the log likelihood, these parameters are stored within the mle object but they are not used by the ?confint" method, which retrieves their value from the global environment (whenever they still exist). Sample code: >