similar to: Infinite series

Dear R-users, I'm estimating a two- dimensional state-space model using the FKF package. The resulting log likelihood function is maximized using auglag from the Alabama package. The procedure works well for a subset of my data, but if I try to use the entire data set I get the following error message. Error in eigen(a$hessian) : infinite or missing values in 'x' What's even

What are some reliable R functions that can compute the value of a convergent series? David -- David R. Bickel, PhD Associate Professor Ottawa Institute of Systems Biology Biochem., Micro. and I. Department Mathematics and Statistics Department University of Ottawa 451 Smyth Road Ottawa, Ontario K1H 8M5 http://www.statomics.com Office Tel: (613) 562-5800 ext. 8670 Office Fax: (613) 562-5185

Dear all, This is the first time I am posting to the r-devel list. On StackOverflow, they suggested that the strange behaviour of integrate() was more bug-like. I am providing a short version of the question (full one with plots: https://stackoverflow.com/q/55639401). Suppose one wants integrate a function that is just a product of two density functions (like gamma). The support of the

Hello Chris, I am trying to fit a 4 state multi-state model using hidden markov routine in the msm package. 1. initial parameters: twoway4.q <- rbind(c(0, 0.25, 0, 0.25), c(0.166, 0, 0.166, 0.166), c(0, 0.25, 0, 0.25), c(0, 0, 0, 0)) ematrix <- rbind( c(0, 0.01, 0, 0), c(0.01, 0, 0.01,0), c(0, 0.1, 0, 0), c(0, 0, 0, 0)) 2. the model: msm_covariates_sexandage <- msm(state ~

Greetings, I would like to calculate tolerancelimits for a series of predicted values from nonlinear regression models. I've been using the tolerance package but the self-starting functions are not in the derivitive tables. When I spellout the functions and supply starting values I repeatedly get an error message regardless of the starting values I use (see output below). Does anyone

Dear list, I'm calculating the integral of a Gaussian function from 0 to infinity. I understand from ?integrate that it's usually better to specify Inf explicitly as a limit rather than an arbitrary large number, as in this case integrate() performs a trick to do the integration better. However, I do not understand the following, if I shift the Gauss function by some amount the integral

Greetings fellow R entusiasts! We have some problems converting a computer routine written initially for Gauss to estimate a Markov Regime Switching analysis with Time Varying Transition Probability. The source code in Gauss is here: http://www.econ.washington.edu/user/cnelson/markov/programs/hmt_tvp.opt We have converted the code to R, and it's running without errors, but we have some

Hello, I apologize for this question that may has been asked a lot of times but I could not go through it. I create a multivariate time series containing NA values. I want to compute a linear regression and obtain a time serie for both residuals and fitted values. I have tried the trick ts.intersect, without success. Could you help me out of this? #### Example: y<-ts(1:10+rnorm(10))

Hi Justin and all, you've mentioned that you used an easy "trick" for defining an infinite register file in the backend. Does this involve defining a single dummy register, and then adding this dummy reg to each register class? Is anything more needed? I refer to the RegisterInfo.td file, as e.g: llvm-3.0.src/lib/Target/PTX/PTXRegisterInfo.td I'm (just starting)

On Mon, Feb 11, 2013 at 6:32 PM, <nkavv at physics.auth.gr> wrote: > Hi Justin and all, > > you've mentioned that you used an easy "trick" for defining an infinite > register file in the backend. > The original PTX back-end just didn't perform register allocation. All registers emitted in the assembly were virtual registers, mapped to a consecutive range.

Dear R Users, I've never used R before and my professor has asked us to do some pretty intense programming (or it's intense to me at least). Here is the question: Modify the function myquadrature inside the script so that it returns the quadrature of descrete data using the trapezoidal rule. Modify the call to the function at the bottom of the script so that is uses your modifies

Hi R-users, I am trying to estimate function parameters using optim(). My count observations follows a Poisson like distribution. The problem is that I wanna express the lambda coefficient, in the passion likelihood function, as a linear function of other covariates (and thus of other coefficients). The codes that I am using (except data frame) are the following (FYI the parameters need to be

Dear all, Somebody kindly pointed out a problem in my WaveThresh3 code. I can't figure out what is wrong. I have whittled down the code quite a bit into an example case which repeats the problem. There is one R function called "ScalingFunction" and one C function called "CScalFn.c". The idea is that the R function calls the C routine repeatedly to compute a wavelet

Hi, I'm running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on the same models/data, and I got error messages for the gam() model and warnings for the glm() model. R-help suggested that the glm() warning messages are due to the model perfectly predicting binary output. Perhaps the model overfits the data? I inspected my data and it was not immediately obvious to me (though I

Hello R-helpers.. I want to ask about how I can sample data sets without having the infinite numbers coming out. For example, set.seed(1234) a<-rnorm(15,0,1) b<-rnorm(15,0,1) c<-rnorm(15,0,1) d<-rnorm(15,0,36) After come out with the sample, I need to do a transformation (by Hoaglin, 1985) for each data set. Actually I need to measure the skewness and kurtosis, that's why I

Dear R experts, I have been encountering problems with the "optim" routine using "for" loops. I am determining the optimal parameters of several nested models by minimizing the negative Log-Likelihood (NLL) of a dataset. The aim is to find the model which best describes the data. To this end, I am simulating artificial data sets based on the model with the least number

I have a bunch of lists that are essentially time-series with the unit of time being 'day'. So I naturally want to generate a time-series from 1:365. I was wondering if there is a nifty 'R' trick to turn a list with missing data (the list may contain values at 100, 230, and 360) into a time series with the missing data filled in (with zeros). Thank you. Kevin

Hi, Following convention below: y(t) = Ax(t)+Bu(t)+eps(t) # observation eq x(t) = Cx(t-1)+Du(t)+eta(t) # state eq I modified the following routine (which I copied from: http://www.stat.pitt.edu/stoffer/tsa2/Rcode/Kall.R) to accommodate u(t), an exogenous input to the system. for (i in 2:N){ xp[[i]]=C%*%xf[[i-1]] Pp[[i]]=C%*%Pf[[i-1]]%*%t(C)+Q siginv=A[[i]]%*%Pp[[i]]%*%t(A[[i]])+R

Well apparently this has nothing to do with the gini() command. I cannot get it to work for something as simple as sum() Here is the little example I am playing with, maybe someone can help me find my error: a<-c("A","B","C","A","B","C","A","A","C","B")

Dear all, In many papers regarding time series analysis of acquired data, the authors analyze 'marginal distribution' (i.e. marginal with respect to time) of their data by for example checking 'cdf heavy tail' hypothesis. For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.? Are there limit theorems which for example allow us to claim that