search for: nmodels

Dear R-users, I have written the following code to generate some trellis plots. It works perfectly fine except that it is quite slow when it is apply to my typical datasets (over several thousands of lines). I believe the problem comes from the loops I am using to subset my data.frame. I read in the archives that the tapply function is often more efficient than a loop in R. Unfortunately ,

Dear all, I have made couple logistic regressions, what making a distribution of some event. Currently, i store it like this: o1 <- lrm(...) o2 <- lrm(...) o3 <- lrm(...) ... Then, i have made a function to peak required regression object from this variables by it number: get_object <- function(obj_name, nModel) { eval (parse(text=paste("o <- ", obj_name, nModel,

I am running the following nls equation. I tried it with data that excel was fitting and got the error: singular gradient matrix at initial parameter estimates I thought it was due to a low number of points (6), but when I create a dataset, I get the same problem. If I remove the parameter "a," then it can find a solution. Does anyone know what I can do to fit this model?

Hi all! I would like to import a matlab or gauss code to R. Could you help me? Bye, Sebasti?n. 2007/6/23, r-help-request en stat.math.ethz.ch <r-help-request en stat.math.ethz.ch>: > Send R-help mailing list submissions to > r-help en stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit >

Dear all, I have tried to modify the code of MASS:::stepAIC.default(), dropterm() and addterm() to use AICc instead of AIC for model selection. The code is appended below. Somehow the calculations are still not correct and I would be grateful if anyone could have a look at what might be wrong with this code... Here is a working example: ## require(nlme) model1=lme(distance ~ age + Sex, data =

hi, I would like to find the x value (independent variable) for a certain dependent value using the fitted model with nls. with (predict) I can find y that corresponds to a list of x. I need the other way around. can it be done? thanks, afadda

Here is the code I am running: library(nls2) modeltest<- function(A,mu,l,b,thour){ out<-vector(length=length(thour)) for (i in 1:length(thour)) { out[i]<-b+A/(1+exp(4*mu/A*(l-thour[i])+2)) } return(out) } A=1.3 mu=.22 l = 15 b = .07 thour = 1:25 Yvals<-modeltest(A,mu,l,b,thour)-.125+runif(25)/4 st2 <- expand.grid(A = seq(0.1, 1.6,.5), mu = seq(0.01, .41,.1), l=1, b =seq(0,.6,.3))

...e)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j]>10){ k <- k+1 vlr[k] <- delta[j] } cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n") cat("\nMODELS\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j] <= 2){ k <- k+1 vlr[k] <- j } cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(d...

I've been trying with no success to model mixtures of Gamma distributions using the package flexmix (see examples below). Can anyone help me get it to model better? Thanks very much. -Ben ## ## Please help me get flexmix to correctly model mixtures of ## Gamma distributions. See examples below. ## library('flexmix') ## ## Plot a histogram of dat and the Gamma mixture model given

I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider

...e)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j]>10){ k <- k+1 vlr[k] <- delta[j] } cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n") cat("\nMODELS\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j] <= 2){ k <- k+1 vlr[k] <- j } cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(d...

I think the code is part of the RTAQ package but is not included in it, as I obtained it from https://r-forge.r-project.org/scm/viewvc.php/pkg/RTAQ/R/HAR_model.R?view=markup&root=blotter&sortby=author&pathrev=1028. It is not my code and I make no claim to other's good work, and apologize if I should even be posting it I am not sure, but in the transform function it allows to

I am working with xts dependent data, and my code is as follows (the problem is explained throughout): dat <- getdat("prices") dat <- read.zoo(dat, sep = "",format="%d/%m/%Y %H:%M", tz="", FUN=NULL, regular=TRUE, header=TRUE, index.column=1, colClasses=c("character", "numeric")) dat <- as.xts(dat)