similar to: curve fitting with given term

Hello. How can I compute the Bootstrap p-Value for a one- and two sided test, when I have a bootstrap sample of a statistic of 1000 for example? My hypothesis are for example: 1. Two-Sided: H0: mean=0 vs. H1: mean!=0 2. One Sided: H0: mean>=0 vs. H1: mean<0 I hope you can help me Thanks in advance Regards, Andreas

hi, is there an option to calculate the 'within' & 'between' group variances for a simple ANOVA (aov) model (2 groups, 1 trait, normally distr.) ? or do I have to calculate them from the Sum Sq ? thanks for your time and greetings, gregor -- Gregor Rolshausen PhD Student; University of Freiburg, Germany e-mail: gregor.rolshausen at biologie.uni-freiburg.de tel. :

hi, I am running R 2.7.2 Windows - recently the GUI-windows (for example to "change directory" or "read in R-scripts") do not open - well, they open, but close down directly afterwards. I can't select a directory or file ... I tried re-installing R - same problem again. does anybody have an idea? thanks gregor -- Gregor Rolshausen PhD Student; University of

hello. I have a question on the interpretation of a logistic model. is it helpful to exponentiate the coefficients (estimates)? I think I once read something about that, but I cannot remember where. if so, how would be the interpretation of the exp(estimate) ? would there be a change of the interpretation of the ANOVA table (or is the ANOVA table not really helpful at all?). thanks for your

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

Dear list, I'm trying to fit a chapman-richards equation to my data, only I cannot interpret the parameters a, b and d. I know that the parameter b denotes the asymptote, but for the others I couldn't figure out. But I do need to know this in order to set my starting values. Here's the model: modPoplar<- nls(Diameter ~ d*(1-exp(-b *Age))^a ,start=list(a=20,b=0.9,d=33)) I attached

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 mention type model. The errors I get (which are dependant on

Hi folks, I've looked through the list archives and online resources, but I haven't really found an answer to this -- it's pretty basic, but I'm (very much) not a statistician, and I just want to check that my solution is statistically sound. Basically, I have a data file containing two columns of data, call it data.tsv: year count 1999 3 2000 5 2001 9 2002 30 2003 62 2004 154

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

What am I failing to understand here? The script below works fine if the dataset being used is DNase1 <- DNase[ DNase$Run == 1, ] per the example given in help(nlrob). Obviously, I am trying to understand how to use nls and nlrob to fit curves to data using R. #package=DAAG attach(codling) plot(pobs~dose) #next command returns 'step factor reduced below min factor

Dear R users, I am writing to you because I have a few question on how to fix the error term variances in lme in the hope that you could help me. To my knowledge, the closest possibility is to fix the var-cov structure, but not the whole var-cov matrix. I found an old thread (a few years ago) about this, and it seems that the only alternative is to write the likelihood down and use optim or a

Hi R help, I am trying to determine how nls() generates a function based on the self-starting SSlogis and what the formula for the function would be. I've scoured the help site, and other literature to try and figure this out but I still am unsure if I am correct in what I am coming up with. ************************************************************************** dat <-

Hello! Does someone know how to produce L(y|mu) with plotmath? Some code with unsuccessfull results: plot(dnorm(x = seq(from = -4, to = 4, by = 0.1)), type = "l") ## Not what I want legend(legend = c(expression(L(y:mu))), x = "topright") ## Strange, is this a bug? legend(legend = c(expression(L(y|mu))), x = "top") ## Group produces an error legend(legend =

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

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

Hi R users, I have a question about fitting a cosine curve. I don't know how to set the approximate starting values. Besides, does the method work for sine curve as well? Thanks. Part of the dataset is in the following: y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67, 17.62, 17.81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10) t=c(7, 37, 58, 79, 96,

Hi! I have a problem of curve fitting. I use the following data : - vector of predictor data : 0 0.4 0.8 1.2 1.6 - vector of response data : 0.81954 0.64592 0.51247 0.42831 0.35371 I perform parametric fits using custom equations when I use this equation : y = yo + K *(1/(1+exp(-(a+b*ln(x))))) the fitting result is OK but when I use this more general equation : y = yo + K

I have the data: for x: 2, 8, 14, 20, 26, 32, 38, 44, 50, 56, 62, 68, 74, for y: 100, 99, 99, 98, 97, 94, 82, 66, 48, 38, 22, 10, 1. y depends on x by equation: y = 100*exp(b*(1-exp(c*x))/c), where b and c are coefficients. I need to find coefficients in this equation for given data. How can I do that by means of R? Thank you for advance. With regard, Dmitry.

Hello, I am trying to do a nonlinear model using the "nls" command in R software. The data I am using is as follows: A<-c(7.132000,8.668667,9.880667,8.168000,10.863333,10.381333,11.059333,7.589333,4.716667,4.268667,7.265333,10.309333,8.456667,13.359333,8.624000,13.571333,12.523333,4.084667 ,NaN,NaN)

Dear R users. I have data observed on the surface of a torus, and am trying to fit the nonlinear regression using the geodesic equation on a torus. Could anyone give me a helpful advise on this problem? I would definitely appreicate your reply. Sincerely, SUNGSU KIM [[alternative HTML version deleted]]