similar to: problem with gls : combining weights and correlation structure

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)

Hi out there, I am trying to fit a species accumulation curve (increase in number of species known vs. sampling effort) for multiple regions and several bootstrap samples. The bootstrap samples represent different arrangements of the actual sample sequence. I fitted a series of nlme-models and everything seems OK, but since the observations are correlated I tried to include some correlation

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

[hoping to redeem myself for my last spurious bug report] From ?corRatio: Letting d denote the range and n denote the nugget effect, the correlation between two observations a distance r apart is (r/d)^2/(1+(r/d)^2) when no nugget effect is present and (1-n)*(r/d)^2/(1+(r/d)^2) when a nugget effect is assumed. This disagrees with the C code (corStruct.c) /* Rational class

Greetings This is a long email. I'm struggling with a data set comprising 2,278 hydroacoustic estimates of fish biomass density made along line transects in two lakes (lakes Michigan and Huron, three years in each lake). The data represent lakewide surveys in each year and each data point represents the estimate for a horizontal interval 1 km in length. I'm interested in comparing

Dear Users, >From previous analysis (semi-variograms using package gstat), I found spatial autocorrelation in my dataset. The best fitted model to this spatial correlation structure is the Gaussian model (Spherical, Exponential, Linear tested and comparison done by Sum of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option

I'm trying to do a simple (?) analysis of a 1D spatial data set, allowing for spatial autocorrelation. (Actually, I'm comparing expected vs. observed for a spatial model of a 1D spatial data set.) I'm using models like gls(obs~exp,correlation=corExp(form=~pos),data=data) or gls(obs~exp,correlation=corLin(form=~pos),data=data) This form is supposed to fit a linear model of

Dear all, I am trying to simulate data sets from a model fitted with glmmPQL, in order to compute the distribution of a summary statistics. My data are binomial and I have a correlation term in my model. My model is structured in the following way m <- glmmPQL( fixed = cbind(sucess,failure) ~ x1 + x2 + ... , random = ~ 1 | bidon, correlation = corGaus(form=~

Hi R users! I will try to state my question again. I have longitudinal data and fitted the following model with lme: Y = X*beta + U + W(t) + Z where U ~ N(0, nu*I) I is the identity matrix, so this is the random intercept W(t)~ N(0, sigma*H) and H is a matrix which incorporates a Gaussian serial correlation (covariance) in the offdiagonal

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

I am trying to fit a B-spline regression model with a corStruct using gls. I am using bs() and specifying the knots myself. If I make the knots data-dependent, this works but has undesirable side-effects. I prefer to reference an auxilliary variable "knots" in my model formula. It should not be part of the data frame, as it is a vector of a different length. How can this be done? The

  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

Hi, I am trying to plot the original data with the line of the model using the predict function. I want to add SE to the graph, but not sure how to get them out as the predict function for gls does not appear to allow for SE=TRUE argument. Here is my code so far: f1<-formula(MaxNASC40_50~hu3+flcmax+TidalFlag) vf1Exp<-varExp(form=~hu3) B1D<-gls(f1,correlation=corGaus(form=Lat~Lon,

Hello, I have multiple observations (replicates) per station...and the stations are distributed in space. I've applied models using lme and gls from the nlme library. I've messed around with compound symmetry to capture the correlation within stations. My question is.....I have x and y coordinates for each station (the subsamples have nearly the same coordinates)...how do you

Hello All, This is my third time attempting to post this message. I don't see it in the archive, so I'm guessing it is not getting through. If I am wrong, my apologies. I am trying to do a GLS regression, (X'V^-1X)^-1X'V^-1y, using the gls() function from the nlme package. I have the covariance matrix V. I have been searching for a way to specify the correlation structure

Dear list, Inspecting residuals of my linear models, I detected spatial autocorrelation. In order to take this into account, I decided to use the GLS method with the correlation = corGaus ( ~ X + Y). Then, I can sort my GLS models based on their AIC. But ... how to know the proportion of the variance explained by the best one (it can be best of the worst models) ? R-squared value has not the

Dear All, I have just updated R to the version 2.6.2 on F8 (with the official F8 rpm). However, when running as root the following command: update.packages(checkBuilt=T) I get a bunch of errors like the ones below. Any ideas? Thanks in advance, Paul ----------------------------------------------- * Installing *source* package 'nlme' ... ** libs gcc -m32 -std=gnu99 -I/usr/include/R

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

I am unable to compile R packages from source on Ubuntu dapper/edgy. Does anybody have an experience? When I do install.packages("nlme",depend=TRUE), I get following error messages. Can someone help? I get similar messages for any package I try to compile. VR ------------- * Installing *source* package 'nlme' ... ** libs gcc -I/usr/share/R/include -I/usr/share/R/include