search for: asymptote

Dear R Helpers, I try 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,

...gistic 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 as x goes to infinity and theta2 the horizontal asymptote as x goes to -infinity. When I use the function SSfpl(input,A,B,xmid,scal), I'm not sure why the value of A is the lower of the two asymptotes if according to the original function A should be equal to theta1 (upper asymptote).. or maybe...

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

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 this formula, because i don't fine information or lecterature about solution of this!!! Can i help me Hi Mr. Calandra

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>.attia@ag.tamu.edu Cell phone:

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

...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 of 400 observations each of a one-sided Cusum of independent normal increments with mean EZ[t] = (-0.1) and unit variance. Started with any initial value, the mean of the Cusum should approach an asymptote as the number of...

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?o precisa do teu

I'm trying to use a paper by Hardin & Rocke: http://handel.cipic.ucdavis.edu/~dmrocke/Robdist5.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

...pt is below, for interest. My question is whether this is a problem that I should deal with through fixing my script somehow, or whether fitting a nonlinear model to data that may have an underlying linear pattern is inherently statistically wrong (I thought that I could use the estimate of the asymptote as a metric of how linear versus asymptotic the data were). Many thanks, in advance. roly russell - oregon state university PS - I have not consulted Bates & Watts yet; I will. # build artificial data with multiplicative error Dat <- NULL; Dat$x <- rep(seq(1,25,by=3), 5...

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

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 <-

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] tends initially to drop dramatically before starting to climb again to the asymptote. Q[i] is not stationary, as I would naively expect. A simple script follows. I get similar behavior for diffe...

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

...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 the horizontal to intersect the y-axis ? I also want to calculate the value at this y-axis intersection point. Thanks, Mohan DISCLAIMER:\ ===============...{{dropped:31}}

---------- Forwarded message ---------- From: <r-help-owner@r-project.org> Date: Mon, Jan 14, 2013 at 12:31 AM Subject: Help with nonlinear regression To: ahmedatia80@gmail.com The message'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

I have the following binary data set: Sex Response 0 1 0 159 162 1 4 37 My commands library(MASS) sib.glm=glm(sib~sex,family=binomial,data=sib.data) summary(sib.glm) The coefficients in the output are Estimate Std. Error z value Pr(>|z|) (Intercept) -3.6826 0.5062 -7.274 3.48e-13