similar to: Harmonic regression in R

Dear all: Does anyone has harmonic regresssion analysis package written in R (to be used in Windows platform) ? Thanks _______________________________________ YM - Â÷½u°T®§ ´Nºâ§A¨S¦³¤Wºô¡A§AªºªB¤Í¤´¥i¥H¯d¤U°T®§µ¹§A¡A·í§A¤Wºô®É´N¯à¥ß§Y¬Ý¨ì¡A¥ô¦ó»¡¸Ü³£ÉN¨«¥¢¡C http://messenger.yahoo.com.hk [[alternative HTML version deleted]]

Hello, I will try to explain the problem, sorry if it will be a little long... I'm using R to analyze results of cyclic mechanical testing, like this: - apply quasi-sinusoidal load - measure quasi-sinusoidal vertical and horizontal deformations (quasi-sinusoidal load means that load "should be" sinusoidal, but testing machine puts in some noise...) I enclose a sample of data at

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Hello, I try to synthesize harmonic functions with a subset of frequencies determined by fast fourier transform. I wrote two different functions, a looped version and a matrix multiplication version. As an example the looped version takes 5 sec and the matrix-version takes 3-4 sec (R 1.4, Athlon 1.2 GHz, 256 MB, Win ME), but the latter needs huge amount of memory due to the matrices. So, none of

Dear R Users, I have a CO2 time series. I want to fit this series seasonal cycle and trend with fourth harmonic function, and then compute residuals. I am doing something like: file<-read.csv("co2data.csv") names(file) attach(file) fit<-lm(co2~1+time+I(time^2)+sin(2*pi*time)+cos(2*pi*time)+sin(4*pi*time)+cos(4*pi*time)+

Dear All, I have some time series data where X=month and Y=nutrient concentration (I can have several concentration data for one month). Is there a way to fit for it an Harmonic Function. Is there a package, script,etc which I can use? Thx -- View this message in context: http://r.789695.n4.nabble.com/Fitting-a-Harmonic-Function-to-Time-Series-Data-tp3901266p3901266.html Sent from the R help

I am looking for a package to perform harmonic analysis with the goal of estimating the period of the dominant high frequency component in some mono-channel signals. I guess there are presumably a number of CRAN packages allowing for such analysis. However, my search with keywords was not successfull. It brought up a lot of Fourier miscellanea but nothing specifically geared for my needs. I would

Hello list; I am working with a dataset containing animal locations over time for a large number of individuals. I would like to compute a center of activity by finding the minimum harmonic mean on a grid overlaying the points of an animal (a "standard" to express center of activity in animal ecology). I have searched the archives at Jonathan Barons server but have not been able

Hi, Sorry for the very short explanation about the problem of intersection. I have a wave function monitored from the heart beat in a particular interval of times.?Apart?fom that, there is?a line with positive slope (e.g: y = x+2) which lies across the wave and intersect on a number of points. My problem is i have no exact equation for?such a?complex harmonic wave produced by the heart

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

An idea would be like for WMA 9 lower bitrates (32-42-48Kbps) to use a 'crystallizer'; which is basically a harmonics booster focussed at transposing sharp tones some octaves higher. Eg: A file has been recorded @ 20khz computer (or 10khz real life) to preserve space. While playing back the file sounds a bit mushy, almost as if someone was speaking through a cardboard wall. The higher

How to test the statistical significance of the difference of two univariate Linear Regression betas? Hi all, There are two samples of data: D1 and D2. On data D1 we do a univariate Linear Regression and get the coefficient beta1. On data D2 we do a univariate Linear Regression and get the coefficient beta2. How do I test the statistical significance of (beta1-beta2)? Could you please

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will

Hello, I have done some research about breakpoints (I am not a statistician) and I found out about the breakpoint, strucchange and segmented packages in R allowing to find breakpoints assuming linear model. However, I would like to fit a periodic time series with a non linear (periodic) model, and I was wondering how I could find breakpoints for this model in R. Is it even possible ? My model

Good day: I am trying to use readcsvIts("nwr_data_qc.txt",informat=its.format("%Y%m%d%h%M %Y"),header=TRUE,sep="",skip=0,row.names=NULL,as.is=TRUE,dec=".") to read in a file (nwr_data_qc.txt) that looks like this: Time Y M D H Min CO2 2000.18790 2000. 3. 9. 18. 30. 373.60 2000.20156 2000. 3. 14. 18. 30. 373.34

I was looking for suggestions as to how to smooth a timeseries and, having accomplished that, how to find the fitted curve values for intermediate points. ? I've tried numerous examples of possible approaches in R that I've found on the web, but when applied to my simple data, R returns an error message in numerous cases. ? The main problem seems to be that I have monthly data, starting

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Hi everyone. I'm having some troubles with STL function to decompose some data. My issue is that I have monthly data from September 2005 up to August 2010 i. e. 60 observations. I define it in the following way: *u<-read.csv("C:/CELEBREX.csv",header = TRUE) u.ts<-ts(u, start=c(2005,9), frequency=12) * The issue is that when I try to use stl(u.ts, 'per') Then the