similar to: memory allocation

Kevin Wright wrote: > library(nlme) > m2 <- gnls(conc ~ t1*(1-t2*exp(-k*time)), > data = df.Chloride, > start = list( > t1 = 35, > t2 = 0.91, > k = 0.22)) So my error was to use nls instead that gnls. Thanks a lot, Kevin. > summary(m2) > plot(m2) > lag.plot(resid(m2), do.lines=FALSE) >

> myfit1 <- gls(col1 ~ col2+col3+col4+col5+col6-1, data=data2, corr=corAR1( 0.3202), method='ML') Error: cannot allocate vector of size 199712 Kb if I get rid of the "corr=corAR1(0.3202)" option, it works okay... can anybody help me? thanks a lot! [[alternative HTML version deleted]]

Hi R users, I'm here trying to understand correlated residuals in nonlinear estimation. I'm reading/studying the book Bates, D. M. and D. G. Watts, (1988), /Nonlinear regression analysis and its applications/, Wiley, NY. pages 92-94, trying to reproduce the figures and to find out the code in R to perform the necessary calculations. I also consulted Pinheiro and Bates, but without

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti=

Dear list, I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter? International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Dear R-users: I have some problems working with lme function, and i would be glad if anyone could help me. this kind of analysis i was used to do with PROC MIXED from SAS, but i would like to move to R, for many reasons... So, the problem is: Imagine the I have 3 factors: fact_A, fact_B and fact_C: The latter I would assume that is random, and the rest of them are fixed. Analysing the

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs

Dear R community, I used a linear mixed model (named lm11) to model daily soil temperature depending upon vegetation cover and air temperature. I have almost 17,000 observations for six years. I can not account for autocorrelation in my model, since I receive the error message after applying the function: update(lm11, corr=corAR1()) Error: cannot allocate vector of size 220979 Kb Do

Dear R community, I used a linear mixed model (named lm11) to model daily soil temperature depending upon vegetation cover and air temperature. I have almost 17,000 observations for six years. I can not account for autocorrelation in my model, since I receive the error message after applying the function: update(lm11, corr=corAR1()) Error: cannot allocate vector of size 220979 Kb Do

I am attempting to model data with the following variables: timepoint - n=48, monthly over 4 years hospital - n=3 opsn1 - no of outcomes total.patients skillmixpc - skill mix percentage nurse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

I am trying to specify a mixed model for my research, but I can't quite get it to work. I've spent several weeks looking thru various online sources to no avail. I can't find an example of someone trying to do precisely what I'm trying to do. I'm hoping some smart member of this mailing list may be able to help. First off, full disclosure: (1) I'm an engineer by trade, so

Hi there fellow R-users, I am trying to follow an example of modelling a serial correlation structure in the textbook "Mixed Effects Model in S and Splus". However, I am getting some very odd results. Here is what I am trying to run: library(nlme) data(Ovary) fm1<-lme(follicles~sin(2*pi*Time)+cos(2*pi*Time),data=Ovary,random=pdDiag(~s in(2*pi*Time))) ### The example is fine up

Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each

Hi I am comparing four different linear mixed effect models, derived from updating the original one. To compare these, I want to use anova(). I therefore do the following (not reproducible - just to illustration purpose!): dat <- loadSPECIES(SPECIES) subs <- expression(dead==FALSE & recTreat==FALSE) feff <- noBefore~pHarv*year # fixed effect in the model reff <-

Dear users I am fitting a Generalized Additive Mixed Models (gamm) model to establish possible relationship between explanatory variables (water temperature, dissolved oxygen and chlorophyll) and zooplankton data collected in the inner and outer estuarine waters. I am using monthly time-series which are auto-correlated. In the case of the inner waters, I have applied satisfactoryly (by

Hello listmembers, I've been thinking of using gls in the nlme package to test for serial correlation in my data set. I've simulated a sample data set and have found a large discrepancy in the results I get when using the default method REML vs. ML. The data set involves a response that is measured twice a day (once for each level of a treatment factor). In my simulated data set, I

Hi, I am trying to answer the see if density.km (response) is affected by Direction (continuous, integer), Layer (nominal with 12 levels) and direction (nominal with 8 levels). There is an interaction between Layer and Direction. Platform.field is a list of 9 different platforms and is being treated as a random effect. I had previously ran this model without the correlation argument and

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML