similar to: Deviance and AIC in weighted NLS

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1)

Dear list, I have encountered a problem with predict.nls (Windows XP, R.2.5.0), but I am not sure if it is a bug... On the nls man page, an example is: DNase1 <- subset(DNase, Run == 1) fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), data = DNase1, start = list(xmid = 0, scal = 1)) alg = "plinear", trace =

Hi! When checking validity of a model for a large number of experimental data I thought it to be interesting to check the information provided by the summary method programmatically. Still I could not find out which method to use to get to those data. Example (not my real world data, but to show the point): [BEGIN] > DNase1 <- subset(DNase, Run == 1) > fm1DNase1 <- nls(density ~

Hello, How do I specify the formula and random effects without a startup object ? I thought it would be a mixture of nls and lme. after trying very hard, I ask for help on using nlme. Can someone hint me to some examples? I constructed a try using the example from nls: #variables are density, conc and Run #all works fine with nls DNase1 <- subset(DNase, Run == 1 ) fm2DNase1 <- nls(

Hola amigos, Supongamos que se quiere ejecutar un modelo no lineal con nls. Pensemos en el ejemplo de la ayuda: DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) summary(fm1DNase1) Aquí se está modelando la densidad óptica de un ensayo relacionada de forma no lineal (logística) con (el logaritmo) de la concentración de una proteína.

Dear R-helpers, does anyone know of some package/function that can build a network from the functions that are implemented in a package, i.e. visualize the cross-references from one function to another in the same or some dependent package? An example would be a function like 'nls' on top of the hierarchy and then a network of nodes from the functions that are called within 'nls'

Dear sirs It seems like there is a bug in `profile.nls` with `algorithm = "plinear"` when a matrix is supplied on the right hand side. Here is the bug and a potential fix ##### # example where profile.nls does not work with `plinear` but does with # `default` require(graphics) set.seed(1) DNase1 <- subset(DNase, Run == 1) x <- rnorm(nrow(DNase1)) f1 <- nls(density ~ b1/(1 +

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

Dear R-helpers, I´m in the context of writing a general function for error propagation in R. There are somehow a few questions I would like to ask (discuss), as my statistical knowledge is somewhat restricted. Below is the function I wrote, the questions are marked. Many thanks in advance. propagate <- function(expr, varList, type = c("stat", "raw"), cov = TRUE) {

Hi, I am trying to run the following nonlinear regression model. > nreg <- nls(y ~ exp(-b*x), data = mydf, start = list(b = 0), alg = "default", trace = TRUE) OUTPUT: 24619327 : 0 24593178 : 0.0001166910 24555219 : 0.0005019005 24521810 : 0.001341571 24500774 : 0.002705402 24490713 : 0.004401078 24486658 : 0.00607728 24485115 : 0.007484372

Hello! I work with : R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) On Windows XP Professional (Version 2002) SP2. At this moment I use the function "nls" combined with a selfStar model (SSmicmen, related to Michaelis-Menten equation, and provided by the "stats" package). When I realise the following operation (cf. p 59 of the

Hi all, I try to write a simple function in a script. The script is as follows yo<-function(Xdata) { n<-length(Xdata[,1]) Lgm<-nls(formula=LgmFormula, data=Xdata, start=list(a=1500,b=0.1),weights=Xdata$Qe) return(Lgm) } After the execution of the script, when I call the function yo on data called NC60.DATA I get an error. #yo(NC60.DATA) Erreur dans eval(expr, envir, enclos)

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

I?m new to R so maybe this issue has been asked before and I still could not read the complete set of past messages sent to the list. I found a weird behabiour that I will explain with a simple example. Lets consider the following block of commands: > x <- diag(c(1,4,10)) > x [,1] [,2] [,3] [1,] 1 0 0 [2,] 0 4 0 [3,] 0 0 10 > invx <- x^-1 > invx

A maybe trivial and stupid question: In the case of a lm or glm fit, it is quite informative (to me) to have a look to the null deviance and the residual deviance of a model. This is generally provided in the print method or the summary, eg: Null Deviance: 658.8 Residual Deviance: 507.3 and (a bit simpled minded) I like to think that the proportion of deviance 'explained' by the

Dear R users, I read your methods of extracting the variance explained by each predictor in different places. My question is: using the method you suggested, the sum of the deviance explained by all terms is not equal to the deviance explained by the full model. Could you tell me what caused such problem? > set.seed(0) > n<-400 > x1 <- runif(n, 0, 1) > ## to see problem

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Hi Everyone, I'm working on building some classification trees, and up to this point I've been using rpart. However, I recently discovered the tree package, and found that it had some useful functions (in particular deviance(), which I would really like to use for my project). I can't seem to find an equivalent function for rpart. I've considered using tree() in place of