search for: yules

Dear R-Users, I need functions to calculate Yule's Y or Cram?rs Index, in order to correlate variables that are nominally scaled? Am I wrong? Are such functions existing? Sincerely, Timo

Hi useRs, I'm trying to fit a basic AR(2) model with the 'ar' function. And when I try to check the value of the coefficients, I could not find the same value as the 'ar' function. Here is my example: myserie <- c(212, 205, 210, 213, 217, 222, 216, 218, 220, 212, 215, 236) #plot(myserie, type="l") myserieminus0 <- tail(myserie, -2) myserieminus1 <-

Hi useRs, I'm trying to fit a basic AR(2) model with the 'ar' function. And when I try to check the value of the coefficients, I could not find the same value as the 'ar' function. Here is my example: myserie <- c(212, 205, 210, 213, 217, 222, 216, 218, 220, 212, 215, 236) #plot(myserie, type="l") myserieminus0 <- tail(myserie, -2) myserieminus1 <-

Dear R Users, Is anyone aware of a package which calculates the Yule Kendall resistant (to errors,outliers) measure of skewness ? An easy calculation to perform, but was just wondering if a package exists (as the contents of that package would probably include other cool things I would also be interested). Thanks, Tolga Generally, this communication is for informational purposes only and it

Hi I am trying this > x <- read.table("/home/kenji/1245/GDS1_2grps_.cls", header = F, skip = 2) > x <- read.table("/home/kenji/1246/MYCset.cls", header = F, skip = 2) Warning message: In read.table("/home/kenji/1246/MYCset.cls", header = F, skip = 2) : incomplete final line found by readTableHeader on '/home/kenji/1246/MYCset.cls' Here are the

I have problem with integer overflow that I cannot understand. I have a character vector curr.lemmas with the following properties: length(curr.lemmas) # 61224 length(unique(curr.lemmas)) # 2652 That vector is the input to the following function: yules.k1 <- function(input) { m1 <- length(input); temp <- table(table(input)) m2 <- sum("*"(temp, as.numeric(names(temp))^2)) return(10000*(m2-m1) / (m1*m1)) } When I run this, I get the following output: [1] NA Warning message: In m1 * m1 : NAs produced by integer overf...

Dear list, according to the documentation of acf{ts} "the partial correlation coefficient is estimated by fitting autoregressive models of successively higher orders up to lag.max. " However, R seems to return the Yule-Walker estimates of the PACF by default. You can check this using c(1:10) as the series: the YW estimates are 0.7000000 and -0.1527035 for lags 1 and 2 . If the PACF

...1224L). c) 61224*61224 > 2^31-1 so that answer cannot fit into an integer. d) Exponentiation is a floating point operation so the result of 61224L^2L is a floating point answer that CAN fit into the 53bit mantissa of a double precision value, so no overflow occurs. e) Defining a function like yules.k1 and never showing how you called it does not constitute a reproducible example. To avoid such gaffes you can use the reprex package to confirm that the errors shown in your question are in fact reproducible. f) On this mailing list, the fact that you are using RStudio is at best irrelevant, and...

I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data. This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit

I have coded a time series from simulated data: simtimeseries <- arima.sim(n=1024,list(order=c(4,0,0),ar=c(2.7607, -3.8106, 2.6535, -0.9258),sd=sqrt(1))) #show roots are outside unit circle plot.ts(simtimeseries, xlab="", ylab="", main="Time Series of Simulated Data") # Yule ---------------------------------------------------------------------------- q1 <-

...2^31-1 so that answer cannot fit into an integer. > > d) Exponentiation is a floating point operation so the result of 61224L^2L > is a floating point answer that CAN fit into the 53bit mantissa of a double > precision value, so no overflow occurs. > > e) Defining a function like yules.k1 and never showing how you called it > does not constitute a reproducible example. To avoid such gaffes you can > use the reprex package to confirm that the errors shown in your question > are in fact reproducible. > > f) On this mailing list, the fact that you are using RStudio is...

Dear R People: Here is some output from AR and ARIMA functions: > xb <- arima.sim(n=120,model=list(ar=0.85)) > xb.ar <- ar(xb) > xb.ar Call: ar(x = xb) Coefficients: 1 0.6642 Order selected 1 sigma^2 estimated as 1.094 > xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE) > xb.arima Call: arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)

Brand new to R Want to understand the algorithm used in yule-walker time series autoregression model I assume there is a way to see the source for ar.yw I also assume that everybody except me knows how Could someone suggest to me how to find out I've looked thru some of the documenttion - there's a lot - and apparently I haven't looked the right place. Thanks in advance Dick

--- On Mon, 1/17/11, Raymond Wong <raywong365@yahoo.ca> wrote: From: Raymond Wong <raywong365@yahoo.ca> Subject: Re: [R] help in calculating ar on ranked vector To: "Uwe Ligges" <ligges@statistik.tu-dortmund.de> Received: Monday, January 17, 2011, 11:56 AM Thanks Uwe:   Here is my code. the first set of print statements work, but not the second.   #

...a floating point answer that CAN fit into the 53bit mantissa of a double precision value, so no overflow occurs. Yes, that's all great and I knew that from <https://stackoverflow.com/questions/8804779/what-is-integer-overflow-in-r-and-how-can-it-happen>. > e) Defining a function like yules.k1 and never showing how you called it does not constitute a reproducible example. To avoid such gaffes you can use the reprex package to confirm that the errors shown in your question are in fact reproducible. Responding to a post and never seeing that the provided code does actually show how I ca...

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

I have been trying to get a working version of ar, since I have used it in several calculations in the test suite for my time series library. The following limited version (order.max must be specified and other short comings) works more or less, but the results differ by more than I would expect from those given by Splus. I have tried several variations with no success. If anyone can see a reason

Hi, I'm researching lossless compression for a highschool mathematics research essay and am fairly confused about how the linear prediction coefficients are solved for within flac. As far as I understand, Levinson Durbin Recursion is used to solve for these coefficients, however, what I don't understand is what the toeplitz matrix is composed of. I found sources using samples from within

I have just put up a new executable as a replacement for the one in rseptbeta.zip there have only been a few changes; mostly to the menu's. I am about to start on a major overhaul including getting survival to work and grabbing the 0.60 version once it's stable. Please let me know about other enhancements you want.... robert

I have just put up a new executable as a replacement for the one in rseptbeta.zip there have only been a few changes; mostly to the menu's. I am about to start on a major overhaul including getting survival to work and grabbing the 0.60 version once it's stable. Please let me know about other enhancements you want.... robert