similar to: Parameter estimation in nls

Dear R-helpers, I am using confint() within a function, and I want to turn off the message it prints: x <- rnorm(100) y <- x^1.1+rnorm(100) nlsfit <- nls(y ~ g0*x^g1, start=list(g0=1,g1=1)) > confint(nlsfit) Waiting for profiling to be done... 2.5% 97.5% g0 0.4484198 1.143761 g1 1.0380479 2.370057 I cannot find any way to turn off 'Waiting for. .." I tried

Why fails nls with "singular gradient" here? I post a minimal example on the bottom and would be very happy if someone could help me. Kind regards, ########### # define some constants smallc <- 0.0001 t <- seq(0,1,0.001) t0 <- 0.5 tau1 <- 0.02 # generate yy(t) yy <- 1/2 * ( 1- tanh((t - t0)/smallc) * exp(-t / tau1) ) + rnorm(length(t))*0.01 # show the curve

I have an inquire about the RNG in R It is known that when we use the " rnorm " function , we pass the arguments : 1- number of variables to be generated 2- mean vector of the normal random errors. 3- standard deviation vector of the normal random errors. my question is the following Is the a way (a function) in R that we could specify the covariance matrix in step 3, instead of the

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

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 <-

I am trying to fit a nonlinear regression to infiltration data in order to determine saturated hydraulic conductivity and matric pressure. The original equation can be found in Bagarello et al. 2004 SSSAJ (green-ampt equation for falling head including gravity). I am also VERY new to R and to nonlinear regressions. I have searched the posts, but am still unable to determine why my data come up

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

In order to fit a probability distribution proposed by Sichel [Journal of the Royal Statistical Society. Series A (General), Vol. 137, No. 1. (1974), pp. 25-34], I need a modified Bessel function of the 2nd kind. I notice that the base package of "R" only has modified Bessel functions of the 1st and 3rd kind. Does a modified Bessel function of the 2nd kind exist anywhere? Many

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

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

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

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)

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 <-

Hello, I'm trying to fit experimental data with a model and nls. For some experiments, I have data with x from 0 to 1.2 and the fit is quite good. But it can happen that I have data only the [0,0.8] range (see the example below) and, then, the fit is not correct. I would like to add a constraint, for example : the second derivative must be positive. But I don't know how to add this to

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

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

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.