similar to: Detection Times and Poisson Distribution

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 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'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 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

Hi all, I have written a simple R script to help me analyze a large data set. I would like to have the script pause to allow the user to input a character string that is subsequently used as a filename when saving tables. I have tried to use the "readline" command - this seems to work fine when entering commands one by one, but when I copy and paste the entire script into R,

Hi all, How can a multivariate Poisson time series be modeled? Aspects of glm, forecast, dse and dynlm seem relevant but not quite complete--but hopefully what I am missing is how to assemble them effectively. What I am looking to do is model my dependent variable y_t as a Poisson family function of lags of several independent variables and lags of y_t. I would like to include all lags up

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

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 thought I was finished, having gotten everything to work as intended. This is a model of risk, and the short term forecasts look very good, given the data collected after the estimates are produced (this model is intended to be executed daily, to give a continuing picture of our risk). But now there is a new requirement. I have weekly samples from a non-autonomous process (i.e. although well

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

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

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]]

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

I've been reading some code from an example in a blog post ( http://www.maxdama.com/ here ) and I came across an operator that I hadn't seen before. The author used a <<- operator to update a variable, like so: ecov_xy <<- ecov_xy+decay*(x[t]*y[t]-ecov_xy) At first I thought it was a mistake and tried replacing it with the usual <- assignment operator, but I didn't

Dear R-Help list, I have a nonlinear least squares problem, which involves a changepoint; at the beginning, the outcome y is constant, and after a delay, t0, y follows a biexponential decay. I log-transform the data, to stabilize the error variance. At time t < t0, my model is log(y_i)=log(exp(a0)+exp(b0)) at time t >= t0, the model is log(y_i)=log(exp(a0-a1*(t_i - t0))+exp(b0=b1*(t_i -

Hi Roel, > 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: >

Hi there, I´ve a question concerning the nnet package in the area of survival analysis: what is the final value, which is computed to fit the model with the following nnet-c all: net <- nnet(cat~x, data=d, size=2, decay=0.1, censored=TRUE, maxit=20, Wts=rep(0,22), Hess=TRUE) where cat is a matrix with a row for each record and

Hi Daniel, On Fri, Dec 30, 2016 at 10:55 PM, Daniel Berlin <dberlin at dberlin.org> wrote: >> Right, but we are talking about "when, in the intermediate state, can i >> transform an undef to a different value". >> >> Remember you can only go down the lattice. So you can't make undef >> constant, and then discover it's wrong, and go back up :)

Dear all, Objective: I am trying to learn about neural networks. I want to see if i can train an artificial neural network model to discriminate between spam and nonspam emails. Problem: I created my own model (example 1 below) and got an error of about 7.7%. I created the same model using the Rattle package (example 2 below, based on rattles log script) and got a much better error of about