search for: yule

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

...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 <- tail(head(myserie, -1), -1) myserieminus2 <- head(myserie, -2) ###Yule Walker equations r1 <- cor(myserieminus0, myserieminus1) r2 <- cor(myserieminus0, myserieminus2) #method 1 phihat1 <- r1*(1-r2)/(1-r1^2) phihat2 <- (r2-r1^2)/(1-r1^2) #method 2 bigR <- cbind(c(1, r1), c(r1, 1)) smallr <- c(r1, r2) ressolve <- solve(bigR, smallr) resaryw &lt...

...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 <- tail(head(myserie, -1), -1) myserieminus2 <- head(myserie, -2) ###Yule Walker equations r1 <- cor(myserieminus0, myserieminus1) r2 <- cor(myserieminus0, myserieminus2) #method 1 phihat1 <- r1*(1-r2)/(1-r1^2) phihat2 <- (r2-r1^2)/(1-r1^2) #method 2 bigR <- cbind(c(1, r1), c(r1, 1)) smallr <- c(r1, r2) ressolve <- solve(bigR, smallr) resaryw &lt...

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 inf...

...<- 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 hex's kenji at Yule:~$ hd 1245/GDS1_2grps_.cls 00000000 31 32 20 32 20 31 0a 23 20 43 44 31 39 2b 20 43 |12 2 1.# CD19+ C| 00000010 44 34 30 4c 0a 30 20 30 20 30 20 30 20 30 20 30 |D40L.0 0 0 0 0 0| 00000020 20 31 20 31 20 31 20 31 20 31 20 31 0a | 1 1 1 1 1 1.| 0000002d kenji at Yule:~$ hd 1246/ job...

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 over...

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 really was estimated from running autoregressions, the first coefficient should be 1 and one would expect an error message for highe...

...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, an...

...0133224, 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 <- nls(y ~ a * x^k * b^x, start=list(a=5,k=1,b=3)) (It's a Yule distribution.) However, I keep getting: "Error in nls(y ~ a * x^k * b^x, start = list(a = 5, k = 1, b = 3)) : singular gradient" I guess this has something to do with the parameter start values. I was wondering, is there a fully automated way of estimating parameters which doesn'...

...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 <- cbind(simtimeseries[1:1024]) q2 <- t(q1)%*%q1 s0 <- q2/1204 r1 <- cbind(simtimeseries[1:1023]) r2 <- cbind(simtimeseries[2:1024]) r3 <- t(r1)%*%r2 s1 <- r3/1204 t1 <- cbind(simtimeseries[1:10...

...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 i...

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...

....   # z<-as.vector(na.omit(z)) #remove na nz<-length(z) rz<-rank(z,ties.method="average") # print(ar(z, order.max=1, method="burg")) print(ar(z, order.max=1, method="ols")) print(ar(z, order.max=1, method="mle")) print(ar(z, order.max=1, method="yule-walker")) # # ************************************************************** # print(ar(rz, order.max=1, method="burg")) print(ar(rz, order.max=1, method="ols")) print(ar(rz, order.max=1, method="mle")) print(ar(rz, order.max=1, method="yule-walker")) #...

...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 c...

...E) Coefficients: 1 2 0.8049 0.0834 Intercept: 1.103 (0.2321) Order selected 2 sigma^2 estimated as 0.009756 Those are very close to the ones obtained with the direct inversion method so the fitting is good. Then we tried to apply the other techniques available in R, namely Yule/Walker, Burg, MLE, obtaining different coefficients, which do not give a good fit of the series at all. Since there isnt anything useful in the R help or its interpretation is very complicated to beginners, can anybody please help us telling how to get a reasonable good fit with YW, Burg and MLE,...

...n by Splus. I have tried several variations with no success. If anyone can see a reason I would much appreciate help. Further below is a limited version of acf, which reproduces Splus results a little better. Paul Gilbert ____________ ar.test <-function(x, aic=T, order.max=NULL, method="yule-walker") {if(method=="burg") stop("burg method for ar not yet implemented.") warning(" ar function not complete and not checked.") if (is.vector(x))x <- matrix(x, length(x),1) sampleT <- nrow(x) if (is.null(order.max)) order.max <- r...

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