similar to: Using corStruct in nlme

Dear All, Using:- R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 and Jose Pinheiro, Douglas Bates, Saikat DebRoy and Deepayan Sarkar (2005). nlme: Linear and nonlinear mixed effects models. R package version 3.1-65. on a WINDOWS 2000 machine I am trying to run the scripts from the Mixed Models book and am running into some

  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

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

Dear R helpers, I would like to model temporal trend of biological remains in sediment cores. All samples are temporally auto-correlated and I would like to take this effect into account. Initially I thought that I could use AR(1) or ARIMA functions but these functions only work with regular temporal intervals between samples. Hence I would like to use a continuous time AR(1) that allow

Hi, I have been using the function lme() of the package nlme to model grouped data that is auto-correlated in time and in space (the data was collected on different days via a moving monitor). I am aware that I can use the correlation classes corCAR1 and corExp (among other options) to model the temporal and spatial components of the auto-correlation. However, as far as I can tell, I can only

I noticed that ?corClasses in package nlme does not list corSpatial among the standard classes. This might either be intentional because corSpatial is not "standard" , or it might be simply an oversight that needs correcting. ------------------------------------------ Robert W. Baer, Ph.D. Professor of Physiology Kirksville College of Osteopathic Medicine A. T. Still University of

Hi all, I wrote a piece of code that generates simulated variables. after variable generation I use them in several analyzes. However, when I use a for to repeat the procedure 1000 times I get an erro message in one of the "for" steps, precisely at this time: gls.temp<- gls(y2 ~ x2,correlation=corExp(form=~coord2[,1]+coord2[,2])) # coord 2 are spatial coordinates and the error

Hello: I am new on R, this is the first time I work with it, I am trying to run some R example to get learn of them, but, I have a problem when running the example below: http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/zCO2.html CO2.Qn1 <- CO2[CO2$Plant == "Qn1", ] SSasympOff(CO2.Qn1$conc, 32, -4, 43) # response only Asym <- 32; lrc <- -4; c0 <- 43

Hello, I would like to fit correlation structures with nlme, but R crashes. My data is similar to the "growth of orange trees" example from Pinheiro and Bates (2000), but data are not equally spaced in time, as the last observation is taken after 6 days ( and not 2 as the others). This is the code I'm using: library(nlme)

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

Dear listers, I am trying to model the distribution of fox density over years in the Doubs department. Measurements have been taken on 470 plots in March each year and georeferenced. Average density is supposed to be different each year. In a first approach, I would like to use a general model of this type, taking spatial correlation into account:

Hi there: I'm trying to run a large number of nonlinear regressions on a time series dataset, where the data will be formatted something to the effect of: ObservationID,time,dependentvar I'll have a number of time and dependentvars for each observation, and I want to apply a nonlinear regression to one ObservationID at a time, and I want to have a dataset that is the parameter

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Hi r-help forum, I have been collecting contact data (with proximity logger collars) between a few different species of animal. All animals wear the collars, and any contact between the animals should be detected and recorded by both collars. However, this isn't always the case and more contacts may be recorded on one collar of the two. This is fine, it depends on battery life and other

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

I need some help understanding how on of the example data sets is formatted in the basic R installation. If I load the Mona Loa CO2 data, with the command: > data(co2) I can view the data with: > co2 And the data are in the form of 11 rows labeled as years (1994-2004) and 12 columns labeled (Jan - Dec). This structure appears to be a dataframe, however, if I type the command

Hi- I am trying to fit a log function to my data, with the ultimate goal of finding the second derivative of the function. However, I am stalled on the first step of fitting a curve. When I use the following code: FG2.model<-(nls((CO2~log(a*Time)+b), start=setNames(coef(lm(CO2 ~ log(Time), data=FG2)), c("a", "b")),data=FG2)) I get the following error: Error in

Dear list users, I have trouble with covariance analysis. I measured nitrate concentrations in the soil (NO3) and the percentage of legumes (LEG, continuous), affected by 2 different CO2 concentrations (CO2, discrete). I suspect that CO2 has an effect on LEG and NO3, but also that LEG has an effect on NO3, so this is the formula I wrote to test this: NO3 ~ CO2 + LEG + CO2:LEG Will LEG be