search for: asymptotic

...to write a small package that based on nlme however my code does not work. R always appears this message: Error in eval(expr, envir, enclos) : object "y" not found where y is the response variable. Please help me out! This is my code: require(nlme) AMPmixed<-function(y, x, S1=c("asymptotic","logistic"), random,data, S2=c("asymptotic","logistic"), start) { logist.logist<-function(x,alpha,delta,psi.l,tau.l,gamma,h){ delta+(alpha-delta+gamma*(x-(h-1))/exp(x))/(1+exp(-(x-tau.l)/psi.l))} logist.asymp<-funct...

Dear R users I'm a new user of R and I have a basic question about the 4-parameter logistic model. According to the information from Pinheiro & Bates the model is: y(x)=theta1+(theta2-theta1)/(1+exp((theta3-x)/theta4)) == y(x)=A+(B-A)/(1+exp((xmid-input)/scal)) from the graph in page 518 of the book of the same authors (mixed models in S) theta 1 corresponds to the horizontal asymptote

I made one typo in my previous mail.   May I ask one statistics related question please? I have one query on MLE itself. It's property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is "Consistent" as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it should collapse to "truth" i.e. for large sample size, variance of MLE should be zero. However asymptotic n...

HI I'm a student in chemical engineering, and i have to implement an algoritm about FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical density) y = a + (c - a) /(1+ e[-b(x-m]) where x = ln(analyte dose + 1) y = the optical absorbance data a = the curves top asymptote b = the slope of the curve c = the curves bottom asymptote m = the curve X intercept Have you never seen

Please help getting the R squared value in asymptotic regression model I use the code below model1<-nls(GN1~SSasymp (nrate,a,b,c), data = data.1 ) and R produced the modell coefficients without the R squared value? -- Ahmed M. Attia Research Assistant Dept. Of Soil&Crop Sciences Texas A&M University ahmed <ahmedatia@zu.edu.eg&g...

Dear all, Sorry if this is slightly off the track as far as R is concerned, but I have been using the Savitzky-Golay filter to estimate some derivatives of interest. I am wondering however, if anyone has seen anything in the literature (or has any ideas) of how these estimates perform asymptotically. Does anyone know what the rate of convergence is for these? Thanks, matt.

I have friendship data which is strong skewed. So it doesn't make sense to use maximum likelihood methods for fa and cfa. But I couldn't find any function for asymptotically distributed data for doing a factor analysis. Only: apca() but there is no possibility to allow for factor correlations. The same problem is with sem() I couldn't get any solutions for my model because of the distribution. I get the error: In sem.default(ram = ram, S = S, N = N, param.na...

I wonder if someone can help me understand some counterintuitive simulation results. Below please find 12 lines of R code that theoretically, to the best of my understanding, should produce essentially a flat line with no discernable pattern. Instead, I see an initial dramatic drop followed by a slow rise to an asymptote. The simulation computes the mean of 20,000 simulated trajectories

Hi listers, I have some data (see attachment), and I fitted it to the Asymptotic Regression Model with NLSstAsymptotic(xy). But I want to know the significance of these fits. How can I accomplish this using R? Can anyone suggest some theoretical reading on this subject? Thanks, Miguel -- Miguel Figueiredo IT student / Marine Biologist "Tem calma irm?o que a morte n...

...t5.pdf as a guide for a function to identify outliers in multivariate data. Attached below is a function that is my attempt to reproduce their method and also a test to see what fraction of the data are identified as outliers. Using this function I am able to reproduce their results regarding the asymptotic chi-square method, but not their new method using an asymptotic F method. In particular, the asymptotic F method (and adjusted F method) seem to give critical distances that are too large and therefore no (or few) points are identified as outliers. I have tried unsuccessfully to contact the autho...

Hello. I am trying to fit an asymptotic relationship (nonlinear) to some ecological data, and am having problems. I am interested in the upper bound on the data (i.e. if there is an upper limit to 'y' across a range of 'x'). As such, I am using the nonlinear quantile regression package (nlrq) to fit a michaelis ment...

Hi all, Other than: http://www.stamats.de/F2006.r Are there other good simple examples out there of using the ROptRegTS package (part of the RobASt project)? I'm hoping to plug it in for multivariate regression. Or is this not a good idea? Just trying to find out how it compares to rlm, lts, glm, etc. Hopefully this makes sense, I'm new to the world of statistics and R. Thanks! St0x

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I read the following data in from a CSV file: species,ecoldom,ecolrank,abundance,persistence,behavdom,behavrank,aggrlevel Fv,0.108333333,6,2.5,0,0.351351351,5,0.12195122 Mq,0.114583333,5...

Hi all, I would like to predict some values for an nls regression function (functional response model Rogers type II). This is an asymptotic function of which I would like to predict the asymptotic value I estimated the paramters with nls, but can't seem to get predictions for values of m choice...... This is my script: RogersII_N <- nls(FR~N0-lambertW(attackR3_N*Th3_N*N0*exp(-attackR3_N*(24-Th3_N*N0)))/(attackR3_N*Th3_N),sta...

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Can someone help me understand simulations of a one-sided Cusum? Consider the following: Q[i] = max(0, Q[i-1]+z[i]), z[i] ~ N(offset, 1), with Q[0] = FIR (fast initial response). With offset < 0, mean{Q[i] for fixed i averaged over many simulations} approaches an asymptote as i -> Inf. In simulations with abs(offset) small and FIR close to the asymptote, Q[i]

May I ask one statistics related question please? I have one query on MLE itself. It's property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is Efficient as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it should collapse to "truth" i.e. for large sample size, variance of MLE should be zero. However asymptotic normality says...

Hi, plot(xc, yc, type="l", ylim=c(0,50), xlim=c(0,50), lwd=2, xlab="M", ylab="seconds") abline(a=-think, b=cpustime, lty="dashed", col="red") abline( 2.2, 0, lty="dashed", col = "red") This draws a response time curve an asymptote and a horizontal line. How do I draw a line from the intersection of the asymptote and

...9;s content type was not explicitly allowed ---------- Forwarded message ---------- From: Ahmed Attia <ahmedatia80@gmail.com> To: r-help@r-project.org Cc: Date: Mon, 14 Jan 2013 00:31:41 -0800 Subject: Help with nonlinear regression Hi- I have a question about nonlinear regression. *Asymptotic Regression Model** * * below is the equation of the model (Reference the R book page 662)* y = a - b e^-cx when I compensate x with 50 for instance, I get a very high value does not match the graph produced by the same model (Asymptotic regression model). below is the output analysis from R;...

...on. I have reviewed the MASS pdf file and, seeing no solution there, have tried to get the Venables & Ripley book from the local college libraries but the only copies are out on loan. I suspect there is a simple explanation of the discrepancy, perhaps a modification to account for pre-asymptotic distribution. Or perhaps I misunderstand the application of the confint fuuction in the MASS package. If someone knows the explanation, I'd appreciate it. -- Jerome L. Myers [[alternative HTML version deleted]]