similar to: nls question

Dear Tom, Short answer, if your using spec.pgram(), use the smoothing kernel to get a better estimate at the frequency centered in the bandwidth. If your frequency bin of interest is wider than the bandwidth of the kernel, average across frequencies (I think). The estimate appears to be normalized already. If you are calculating your PSD independently, then oversample (e.g. 2, perhaps 4 or more

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

I've got a covariance matrix that I'd like to index using the dimnames: > vcov1 n0 x0 s n1 n2 n0 82.43824759 1.839505e-02 -4.975196e-01 2.882394e+03 -2.615986e-01 x0 0.01839505 6.134010e-03 -7.695922e-04 -6.373946e+01 6.086321e-03 s -0.49751964 -7.695922e-04 9.638943e-03 3.406594e+02 -3.173671e-02 n1 2882.39407745

Dear R colleagues: Am trying to fit a simple NL model to determine Economical Optimum Nitrogen Rates. The segmented (quadratic + plateau) model only works with some y's, in some cases I get a "singular gradient" error. I'll appreciate any ideas in how to solve the singular gradient error. Thanks, Jose # The following code works using yield2 in the nls model but not using

I have done a fair bit of spectral analysis, and hadn't finished collecting my thoughts for a reply, so hadn't replied yet. What exactly do you mean by normalize? I have not used the functons periodogram or spectrum, however from the description for periodogram it appears that it returns the spectral density, which is already normalized by frequency, so you don't have to worry about

Hi All, I have two time series, each has length 354. I tried to calculate the coherency^2 between them, but the value I got is always 1. On a website, it says: " Note that if the ensemble averaging were to be omitted, the coherency (squared) would be 1, independent of the data". Does any of you know how to specify properly in R in order to get more useful coherency? The examples in

Hello, Fitting a piecewise smooth curve to a set of points (and a piecewise linear function in particular) seems to be a recurring question on this list. Nevertheless, I was not able to find an answer to a question that bothers me. Suppose I have the following data set, and would want to fit it with a piecewise smooth curve, In this model data, one curve is valid for up to 3 and another one for

Hello, I'm still a newbie user and struggling to automate some analyses from SigmaPlot using R. R is a great help for me so far! But the following problem makes me go nuts. I have two spectra, both have to be fitted to reference data. Problem: the both spectra are connected in some way: the stoichiometry of coefficients "cytf.v"/"cytb.v" is 1/2. {{In the SigmaPlot

I have a need to build a time series and there are a couple of aspects about the time series object that are confusing me. First it seems that ts.union is not doing what I would expect. For example: x0 <- rep(0,10) x1 <- rep(1,10) xt0 <- ts(x0, frequency=10) xt1 <- ts(x1, frequency=10) st2 <- ts.union(xt0, xt1) > xt2 Time Series: Start = c(1, 1) End = c(1, 10) Frequency = 10

Dear R list, I have a recurring problem with the languageR package, specifically the aovlmer.fnc function. When I try to run the following code (from R. H. Baayen's textbook): # Example 1: library(languageR) latinsquare.lmer <- lmer(RT ~ SOA + (1 | Word) + (1 | Subject), data = latinsquare) x <- pvals.fnc(latinsquare.lmer,

Dear list, I apologies first for my English, hope you will understand well my question. I am working on 1/2 hour piezometric data, time unit is second. They present daily oscillation when using the spectrum() function. What I am really interested in, is to find the amplitude corresponding to this oscillation. I work with a college using Matlab, and although we apply the same methodology, our

Hola buenas noches, tengo un problema bastante gordo. ¿A alguno le ha dejado de funcionar las funciones pamer.fnc y mcp.fnc con la nueva versión de R? La semana pasada formatee el ordenador y ahora scripts antiguos no funcionan. La cuestión es que me precupa que no funcione el ejemplo de tutorial del autor. Os dejo un script que debería de funcionar y no lo hace

Hi, I have a system of ODE's I can solve with lsoda. Model=function(t,x,parms) { #parameter definitions lambda=parms[1]; beta=parms[2]; d = parms[3]; delta = parms[4]; p=parms[5]; c=parms[6] xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1]) xdot[2] = (beta*x[3]*x[1]) - (delta*x[2]) xdot[3] = (p*x[2]) - (c*x[3]) return(list(xdot)) } I want

Hi all, I'll have to filter Raman spectra and I would like to do so in R. I'm trying to figure out how. Maybe one of you could help me ... This is what I have to do: General: separate the real spectrum (the set of peaks) from two contaminations: a drifting background and (white) noise Constraints (which should help the task): A - the spectrum is positive B - the spectrum is long

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Hi all I have fitted a model usinf nls function to these data: > x [1] 1 0 0 4 3 5 12 10 12 100 100 100 > y [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853 [6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880 [11] 18.553054450 23.722637370 The model fitted is: modellogis<-nls(y~SSlogis(x,a,b,c)) It runs OK. Then I calculate

Buenas tardes, Quisiera obtener el intervalo de confianza (y también intervalos de predicción) para los valores predichos en un modelo nls. ¿Hay alguna manera que no sea por ggplot2 (me interesaría obtener el valor listado -además de en el gráfico-) o por bootstrap? Os copio el código del ajuste del modelo y predicción para los 3 días siguientes: *#Ajuste del modelo* model = nls(formula =

Dear folks (or Dear Professor Bates), I'm quite confused as to the current status of some of the available functions applicable to lmer objects. Following the examples in Baayen, Davidson, Bates (2006), my plan is to run mcmcsamp on a random effect model created by lmer in package lme4, then use the (perhaps outdated) pvals to estimate p-value. But then I couldn't find pvals anywhere.

Hello everyone, This is my first time posting to the list, thanks in advance. I am calculating the smoothed periodogram for the residuals of an AR model that I fit to EEG data. The autocorrelation plot of the residuals shows the series is now approximately white (i.e. ACF = 1 at lag 0, and close to 0 for all other lags). I would like to show that the spectrum of the series is also