[R] Covariates in NLS (Multiple nonlinear regression)

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x

Re: [R] Covariates in NLS (Multiple nonlinear regression)

- is it possible? Thanks, Lindsay -Original Message- From: Gabor Grothendieck [mailto:ggrothendi...@gmail.com] Sent: 25 August 2009 17:53 To: Lindsay Banin Cc: r-help@r-project.org Subject: Re: [R] Covariates in NLS (Multiple nonlinear regression) Try this: RSiteSearch(Weibull regression) On Tue

Re: [R] Comparing model fits for NLME when models are not nested

Hi all, Many thanks for your advice. Lindsay -Original Message- From: Peter Flom [mailto:peterflomconsult...@mindspring.com] Sent: 12 June 2009 11:18 To: Lindsay Banin; 'r-help@r-project.org' Subject: Re: [R] Comparing model fits for NLME when models are not nested Lindsay Banin

[R] Function for AIC or logLIK for nlsList object

Dear R users, Does anybody have a function to calculate logLik or AIC for nlsList objects? After receiving error messages, another user helped me ascertain that this function is not currently written into R. Many thanks Lindsay __

[R] Comparing model fits for NLME when models are not nested

Hi there, I am looking to compare nonlinear mixed effects models that have different nonlinear functions (different types of growth curve)embedded. Most of the literature I can find focuses on comparing nested models with likelihood ratios and AIC. Is there a way to compare model fits when

[R] Error in 1:p : NA/NaN argument when running model comparisons

Hi there, I am trying to compare nonlinear least squares regression with AIC and anova. The simplest model is one nonlinear curve, and in the more complex model I have a categorical variable (producing parameter estimates for four curves). Both models run fine, but when I try to produce an AIC

[R] Error in NLME (nonlinear mixed effects model)

Hi there, I have been trying to fit an NLME to my data. My dataset has two category levels - one is a fixed effect (level1) and one is a random effect (level2), though so far I have only experimented with the highest level grouping (fixed, level1), with the following code: mod1 - nlme(H ~

[R] (no subject)

'minFactor' of 0.000976563. Any ideas? Many thanks in anticipation, LB ** Lindsay Banin School of Geography University of Leeds Leeds, LS2 9JT UK [EMAIL PROTECTED] ** __ R-help@r-project.org mailing list https