similar to: How to get estimate of confidence interval?

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

I just thought of a useful metaphore for the problem I face. I am dealing with a problem in business finance, with two kinds of related events. However, imagine you have a known amount of carbon (so many kilograms), but you do not know what fraction is C14 (and thus radioactive). Only the C14 will give decay events (and once that event has occurred, the atom that decayed will never decay

Hi All, I'm having some trouble assigning arguments inside a function to produce a plot from a model Can anyone help me? Below I've outlined the situation and examples of failing and working code. Regards Mike ## data ## decay.data <- ... behaviors lift reach.uu estimated.conversions.uu total.reach 1 1 432.0083 770 770 0.00 2

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

I finally got (mostly) what I wanted. In an attempt to figure out how to get nls to deal with a non-differentiable function, I had (stupidly) 'simplified' the problem until it became singular. Can I do something to make optim() less sensitive to my initial guess? For this example, I get a lousy solution if I make the initial guess for t0 = min(t) = 0.05. Thanks again, -- Robert Merithew

Dear All, Apologies if my questions are too basic for this list. I am given a set of data corresponding to list of detection times (real, non-integer numbers in general) for some events, let us say nuclear decays to fix the ideas. It is a small dataset, corresponding to about 400 nuclear decay times. I would like to test the hypothesis that these decay times are Poissonian-distributed. What is

Hello, I have a data set of data which is best fit by an exponential decay function. I would like to use a nonlinear weighted least squares regression. What function should I be using? Thank you! [[alternative HTML version deleted]]

Dear All, I hope I am not asking a FAQ. I am dealing with a problem of graph theory [connected components in a non-directed graph] and I do not want to rediscover the wheel. I saw a large number of R packages dealing for instance with the k-means method or hierarchical clustering for spatially distributed data and I am basically facing a similar problem. I am given a set of data which are the

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Hi, I am trying to determine the number of units in the hidden layer and the decay rate using the CVnn2 script found in MASS directory (reference: pg 348,MASS-4). The model that I am using is in the form of Y ~ X1 + X2 + X3... + X11 and the underlying data is time-series in nature. I found the MASS and nnet package extremely useful (many thanks to the contributors). However I am getting

Hello all, When going through the internals of the old C back-end, I see that the CBE encapsulates arrays into a struct. The source code has the following comment to explain this behaviour. // Arrays are wrapped in structs to allow them to have normal // value semantics (avoiding the array "decay"). For example, the CBE translates: @a = common global [10 x i32]

Hi, I am new to R. I am trying to plot the decay of a population over time (0-50yrs). I have the initial population value (5000) and the mortality rate (0.26/yr) and I can't figure out how to apply this so I get a remaining population value each year. In excel (ignoring headings) I would put 5000 in A1, in B2 I would enter the formula A1*0.26, and then in A2 (the next years population) I

I am using the package Multicore/Parallel to do importance sampling. I have 5 cores on my computer. And I have let's say 10 000 particles to generate. What I did was to send 5 particles in each time, calling the package parallel. Which means in all I am calling the parallel command 2000 times. What happens is in the end somewhere along the way I end up with the error message error in fork

Hello, I have what should be an easy question. I'm a new r user and making the transition from menus to the command line so as to do batch processing of tons of data. One of my data streams needs to be detrended. It's a vector of numbers that follows a negative exponential decay. I need to fit a curve to it and use the residuals as an object. The data looks something like this: foo.dat

HiI want to construct a logliikelood function in RHere is the situationy=number of particles emitted in 1 hr period~pois(30)p=probability of detection of radiation particlesx=number of particles detected by a radiation detector~pois(30p)where p~beta(a,1)I have to calculate the loglikehood for a for the range a(2,50)I wish to simulate 100 random samples for each aHere is my code:-m=481n=100x =

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

Hello, I have data like the following: datum <- structure(list(Y = c(415.5, 3847.83333325, 1942.833333325, 1215.22222233333, 950.142857325, 2399.5833335, 804.75, 579.5, 841.708333325, 494.053571425 ), X = c(1.081818182, 0.492727273, 0.756363636, 0.896363636, 1.518181818, 0.499166667, 1.354545455, 1.61, 1.706363636, 1.063636364 )), .Names = c("Y", "X"), row.names = c(NA,

I am using library(nnet) to train up an ANN with what I believe is a moderately sized dataset, but R is complaining about too many weights: --- > nn.1 <- nnet(t(data), targets, size = 4, rang = 0.1, decay = 5e-4, maxit = 200) Error in nnet.default(t(data), targets, size = 4, rang = 0.1, decay = 5e-04, : Too many (1614) weights > dim(targets) [1] 146 2 > dim(data) ## Note

> nnet.fit<-nnet(as.factor(out) ~ ., data=all_h, size=5, rang=0.3, decay=5e-4, maxit=500) # model fitting > summary(nnet.fit) a 23-5-1 network with 126 weights options were - entropy fitting decay=5e-04 HI, Guys, I can not find the manual to describe how the model is built, is there a more detailed description how nnet package works? -- Sincerely, Changbin -- [[alternative

Hi there, I have fitted a sample (with size 20) to a normal and/or logistic distribution using fitdistr() in MASS or fitdist() in fitdistrplus package. It's easy to get the parameter estimates. Now, I hope to report the confidence interval for those parameter estimates. However, I don't find a function that could give the confidence interval in R. I hope to write a function, however,