similar to: Question about syntax in lm function

I am working with a model that I have to estimate a nonparametric function. The model is partial linear i.e. Y=X$\beta$ + f(z) + $\epsilon$ I am using the ' double residual methods' Robinson (1988) Speckman (1988) where I estimate a nonparametric function for each of the parametric variables in terms of the nonparametric one i.e. X[,i]=g(Z)+ u this is done because I need the $E(

Hi There I have a lm object with 4 parameters and I want to test wether 2 parameters are equal using a Wald test (basically b1=b2 or b1-b2 =0). In the help file from R it says that under ANOVA the optional arguments " Terms" or "L" test whether a linear combination is equal to 0. I tried; >anova(m1, Terms = Beta1-Beta2=0) but I get the error: Object " Beta1"

The function of crossprod in R puzzled me. I would like to calculate the cross product of two vectors. According to my text book, it defines like this: a = (ax, ay, az) b = (bx, by, bz) then, the cross product of a and b is: a X b = (ay*bz-az*by, az*bx-ax*bz, ax*by-ay*bz) It can also write in a determinant format. But the crossprod or tcrossprod function in R appeared not calculate the cross

This is my first time to ask for help in the R mailing list, so sorry for my misbehavior. The question is actually an example of the apply function embedded in R. Code is here: > x <- cbind(x1 = 3, x2 = c(4:1, 2:5)) > dimnames(x)[[1]] <- letters[1:8] > x x1 x2 a 3 4 b 3 3 c 3 2 d 3 1 e 3 2 f 3 3 g 3 4 h 3 5 > cave <- function(x, c1, c2) c(mean(x[c1]),

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

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

Dear all, This is potentially a bug in spec.pgram, when the number of samples is odd,spec.pgramreturns a different result withfast = TRUE, the example below contains the two varieties with a reference spectrum calculated manually. the number of returned spectra is also larger (50 compared to 49) whenfast = TRUE x <- rnorm( 99 ) plot(spec.pgram(x, taper = 0 , detrend = FALSE , plot =

Dear R users I have two questions about estimating the spectral power of a time series: 1) I came across a funny thing with the following code: data(co2) par(mfrow=c(2,1)) co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3) co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3) The first plot displays the frequencies ranging from 0 to 6 whearas the second plot displays the same curve but

Hi Copy your messages to r help, others may give you better advice. Strange, I do not get such error. Beside, from m = t(matrix(data = detrend, nrow = 30)) I expect matrix result, but your str shows that m is list. > m<-list(a=rep(NA, 20), b=rep(1,20)) > colMeans(m, na.rm=T) Error in colMeans(m, na.rm = T) : 'x' must be an array of at least two dimensions So as colMeans help

I am trying to use the "kernel" function. To understand how it works I tried out some of the examples. None of them works as shown in the following: > kernel("daniell", 50) # Error in kernel("daniell", 50) : unused argument(s) (50) > kernel("daniell", 10) # Error in kernel("daniell", 10) : unused argument(s) (10) >

Hi All, I am a PhD student in forestry science and I am working with LiDAR data set (huge data set). I am a brand-new in R and geostatistic (SORRY, my background it?s in forestry) but I wish improve my skill for improve myself. I wish to develop a methodology to processing a large data-set of points (typical in LiDAR) but there is a problem with memory. I had created a subsample data-base but

Hi, Hope this does not sound too ignorant . I am trying to detrend and transform variables to achieve normality and stationarity (for time series use, namely spectral analysis). I am using the boxcox transformations. As my dataset contains zeros, I found I need to add a constant to it in order to run "boxcox". I have ran tests adding several types of constants, from .0001

I have a question regarding the adf.test command in the tseries library. I have a vector of time series observations (2265 daily log prices for the OEX to be exact). I also have this same data in first-differenced form. I want to test both vectors individually for staionarity with an Augmented Dickey-Fuller test. I noticed when I use the adf.test command from the tseries library, the general

Hello! I wonder if there is a typo in detrending code of spec.pgram in spectrum.R from stats package. One can see in the code https://svn.r-project.org/R/trunk/src/library/stats/R/spectrum.R . I am afraid there is a typo and the code should look like if (detrend) { t <- 1L:N - (N + 1)/2 sumt2 <- N * (N^2 - 1)/12 for (i in 1L:ncol(x)) x[, i] <- x[, i] -

Hi, Are there any functions in R that could be used to estimate the phase-shift between two semi-sinusoidal vectors? Here is what I have tried so far, using the spectrum() function -- possibly incorrectly: # generate some fake data, normalized to unit circle x <- jitter(seq(-2*pi, 2*pi, by=0.1), amount=pi/8) # functions defining two out-of-phase phenomena f1 <- function(x)

I have a time series of 1095 data corresponding to a daily data of three years. I want to know how to use ma(timeserie, order=??, centre=??) to detect the trend: which order is suitable and what is the difference between centre= true or false. How to avoid these errors: 1-Error in timeserie - trend : ? argument non num?rique pour un op?rateur binaire="non-numeric argument for a binary

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

Hopefully this will not seem too ignorant of a question. I am having a hard time picking out the sources of the differences between: abs(fft(x))^2/length(x) and spec.pgram(x, taper=0, log="no", plot=FALSE) Also from the limited testing that I have done since the DC "frequency" is not returned from spec.pgram how can I tell what has happened to the series when I specify

Dear list, I had some confusion regarding what function too use in order too relate results from spec.pgram() too a chi-square distribution. The documentation indicates that the PSD estimate can be approximated by a chi-square distribution with 2 degrees of freedom, but I am having trouble figuring out how to do it in R, and figuring out what specifically that statement in the documentation

I have an very long, irregularly spaced time series (and I'm also new to spectral analysis, so please be patient.) I want to use spec.ls as an interpolator and then use the output to reconstruct the time series via inverse fft. But so far I've been having difficulty doing this. ts<-read.csv("timeseries.csv",header=TRUE) #file contains over 30000 irregularly spaced