search for: discretisation

...d up in using R for this job, in the first place!) More precisely, does anybody know any technical issue that could make the work insecure in the sense of propagation of errors? Is there any track of evidence that R is, in this kind of task, less reliable than e.g. MatLab? We use just a simple discretisation written in a for loop and a variable (i.e. user-fed) time step. Maybe, I'm too neurotic about this, but I guess I just want some comfort after seeing a few particularly nasty orbits. Best, Markku Karhunen National Public Health Institute, Finland

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? -

Hi, I couldn't figure out how to use the functions from the ctest library. I'm using the r-base package that comes with debian potato. library("ctest") told me that no such package existed. I checked the CRAN, but no such package was availiable, instead I was told that it would be part of the standard installation. But functions from ctest like shapiro-wilk don't work. The

Hi, I'm having a problem using sample() within a function. Basically I get an error reading: Error in sample(v, 1, prob = h) : non-positive probability Can anyone advise me as to the possible origin of this error? Here is my code #Discretised Gillespie algorithm function (From SMfSB, D.J. Wilkinson) gillespied=function (N, T=100, dt=1, ...) { tt=0 n=T%/%dt x=N$M

...riminant analysis with the fda() function, but I don''t know all the details of splines theory. I try on the example of the phonems from the article "Penalised Discriminant Analysis" of Hastie, Buja and Tibshirani 1994 : 5 groups and 256 variables. The 256 variables are from the discretisation of log-periodogram. Consequently, we want the scores to be smoothed by the frequency. in continuus writing, J(beta)= integral of the square second derivative of beta upon the frequencies and J(beta)=(beta)^T*OMEGA*beta No function for producing one dimensional penalty object is providing with ge...

Hi, I want to draw a scatter plot with 1M and more points and save it as pdf. This makes the pdf file large. So i tried to save the file first as png and than convert it to pdf. This looks OK if printed but if viewed e.g. with acrobat as document figure the quality is bad. Anyone knows a way to reduce the size but keep the quality? /E -- Dipl. bio-chem. Witold Eryk Wolski MPI-Moleculare

ggplot2 ------------------------------------------------------------ ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and avoid bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce

ggplot2 ------------------------------------------------------------ ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and avoid bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

Hello I need to plot a histogram, but insted of using bars, I'd like to plot the data points. I've been doing it like this so far: h <- hist(x, plot = F) plot(y = x$counts / sum(x$counts), x = x$breaks[2:length(x$breaks)], type = "p", log = "xy") Sometimes I want to have a look at the "raw" data (avoiding any kind of binning). When x

I have been trying the integrate function in R, a function which would be very useful for a current project of mine. But I am encountering errors integrating the one function I have tried. The function to be integrated is a product of a gamma demsity and a normal density: gamma.by.normal_function(y,x,shape,scale,stdev) return( dgamma(y,shape=shape,scale=scale)*

Hello everyone, I'm trying to use mle from package stats4 to fit a bi/multi-modal distribution to some data, but I have some problems with it. Here's what I'm doing (for a bimodal distribution): # Build some fake binormally distributed data, the procedure fails also with real data, so the problem isn't here data = c(rnorm(1000, 3, 0.5), rnorm(500, 5, 0.3)) # Just to check

...iven by > > > > x[t]=A[t]*x[t-1]+B[t]*epsilon[t] > > y[t]=C[t]*x[t]+eta[t] > > > > A, y, B and C are Matrices. Y[t] is the data input vector with 800 > elements > (every t has one element) > > > > My Model is described by the following > (discretisation<http://www.dict.cc/englisch-deutsch/discretisation.html>) > stochastic differential equation > > > > Lambda[t]=lambda[t-1]+kappa*lambda[t]*delta_t+epsilon_l > > R[t]=R[t-1]+mu*delta_t+epsilon_r > > epsilon_l=sigma_l*sqroot(delta_t) > > epsilon_r=sigma_r*...