similar to: general question about use of list

What is 1. par.ests <- optimfit$par 2. fisher <- hessb(negloglik, par.ests, maxvalue=maxima); 3. varcov <- solve(fisher); 4. par.ses <- sqrt(diag(varcov)); Thanks a lot, fit.GEV <- function(maxima) { sigma0 <- sqrt((6. * var(maxima))/pi) mu0 <- mean(maxima) - 0.57722 * sigma0 xi0 <- 0.1 theta <- c(xi0, mu0, sigma0) #10/5/2007: removed assign() for maxima.nl

Hi, Given that the R forum is mostly a site to discuss issues related to the language, are there any forums that discuss statistical issues? I am specifically interested in the statistical issues in experimental design. TIA, Anjan -- ============================= anjan purkayastha, phd bioinformatics analyst whitehead institute for biomedical research nine cambridge center cambridge, ma 02142

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

r-help at lists.R-project.org ? Hello ? First I want to implement exponential regression in R, with out constant for the following formula. Y = exp(a*X) ?a? is coefficient I wanted to determine. That I could do also in SPSS but my question is rather to estimate the ?standard error of ??i ?at each Xi. This is called in SPSS ?satndard error of mean prediction? or generally known for non-linear

Dear List: I found an error when I called the 'gee' function. I cannot solve and explain it. There are no errors when I used the 'geeglm' function. Both functions fit the gee model. The project supervisor recommends me to use the 'gee' function. But I cannot explain to him why this error happens. Would you help me solve this problem? I appreciate your help. In

Hi, I am looking for documentation, reference guides, etc. that explain the output of functions... For example using cor.test(...., method="pearson") with Pearson's corr coeff the output is: Pearson's product-moment correlation data: a and b t = 0.2878, df = 14, p-value = 0.7777 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval:

Hi everybody, I am currently working on mixtures of two densities ( f(xi,teta)= (1-teta)*f1(xi) + teta*f2(xi) ), particularly on the behavior of the variance for teta=0 (so sample only comes from the first distribution). To determine the maximum likelihood estimator I use the Newton-Rapdon Iteration. But when computing the first derivative I get a none linear function (with several asymptotes)

I am looking for a good probability book that describes convergence in distribution. I have looked through Introduction to Probability by Charles M. Grinstead, J. Laurie Snell, but I don't find any formal description on convergence in distribution. Could somebody recommend a good book that cover this topic? Thank you!

Hi I am trying to use the deriv and eval functions to obtain the value of a function , say "xi-(alpha0+alpha1*gi)" , differentiated with respect to alpha0 and alpha1, in the following way # for gi = 0 > dU1dtheta <- deriv(~ xi-(alpha0+alpha1*gi), c("alpha0","alpha1")) > eval(dU1dtheta) (Intercept) -0.2547153 attr(,"gradient")

Lisa, this seems to be fairly straight forward to do in R and I'm happy to help you get started. However, please be aware that you do have to have knowledge of statistics to do the analysis/modeling. Rolf, Jeff, I do appreciate your view that this is not a R probelm. It's more a 'how to use R' / 'help me get started' problem. The posting guidelines point to "Usenet

Hi, I have been trying to do numerical differentiation using R. I found some old S code using Richardson Extrapolation which I managed to get to work. I am posting it here in case anyone needs it. ######################################################################## richardson.grad <- function(func, x, d=0.01, eps=1e-4, r=6, show=F){ # This function calculates a numerical approximation

I have some question about metafor package. I'm interest to perform a random effect meta-analysis of proportion (single group summary of prevalence of disease in a population as reported by different study) It ask: 1. "PFT": The Freeman-Tukey double arcsine transformed proportion is reported to be equal to 1/2*(asin(sqrt(xi/(ni+1))) + asin(sqrt((xi+1)/(ni+1)))). Hovewer, i

Dear all, I would like to run a simple regression model y~x1+x2+x3+... The problem is that I have a lot of independent variables (xi) -- around one hundred -- and that some of them are categorical with a lot of categories (like, for example, ZIP code). One straightforward way would be to (a) transform all categorical variables into 1/0 dummies and (b) enter all the variables into an lm model.

Hi, I am again asking a generic question and the general response for such questions is cold. I am a beginner but use and write simple R scripts. I am looking for some ideas to calculate the confidence intervals based on this excerpt from the paper. Moreover it would help if someone points to material to read about degrees of freedom and any related concepts. Thanks,

I'm using R 2.8.1 on Ubuntu 8.10. I'm writing partly to ask what's wrong, partly to tell other users who search that there is a work around. The General Social Survey is a long standing series of surveys provided by NORC (National Opinion Research Center). I have downloaded some years of the survey data in SPSS format (here's the site:

Regarding 1 and 2, please read the Posting Guide mentioned at the bottom of every R-help post. R does not equal statistics... and education about statistics is way too ambitious to include in this mailing list that is about a tool that happens to be useful for statisticians. There are forums online that do cater to statistical methods (e.g. Cross Validated or many results from a search engine)...

Hello, I want to model my data with the following model: Y1=X1*coef1+X2*coef2 Y2=X1*coef2+X2*coef3 Note: coef2 appears in both lines Xi, Yi is input versus output data respectively How can I do this in R? I got this far: lm(Y1~X1+X2,mydata) now how do I add the second line of the model including the cross dependency? Your help is greatly appreciated! Cheers, Bastiaan

Up to now, I have recognized problems with "gpd(..)", the function from the package "evir" I think that all these functions that estimate the parameters xi, beta for the GPD by given threshold mu use the function "optim(..)" ( gpd, fitgpd, ...) "Error" example: data1 <- rgpd(1000, xi= -1.5, mu=1000, beta=100) so the created poinnts take place in about

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

Hi, I am trying to fit a Johnson SB distribution using fitdist function in fitdistrplus Library. I have defined the Johnson SB distribution from ( http://www.ntrand.com/johnson-sb-distribution/) . But it gives me the follwing errors. Any help would be appreciated #xi = xi #lambda =l #delta =d #gamma = g djohn = function(x,xi,l,d,g) (d/(l*sqrt(2*pi)*((x-xi)/l)*(1-((x-xi)/l))))*exp[-0.5*(g +