similar to: corSpatial and nlme

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Hello, I am having a problem specifying the correlation structure in lme which leads to the error: Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial". I have specified a grouping variable which is the only fix I could find by searching this error on R-help. ISee the below example. When my samples (tran) - which are transects

  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'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

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

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 all, I have been attempting this repeated measure analysis where correlation among observations (within each subject) is spatial. My command is: >fdat.lme1<-lme(Lint~Rep+Treatment,data=fdat,random=~1|fldPos,correlation=corLin(form=~lat+lon)) It is generating follwoing error: Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial"

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

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

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

Hi, I am trying to place my own functions in the nlme environment: The following statement works: environment(coef.corSPT) <- environment(getS3method("coef","corSpatial")) but this one returns an error: environment(get("coef<-.corSPT")) <- environment(getS3method("coef<-","corSpatial")) Error in

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 have two straightforward questions about linking in the man pages for R packages: First, is it possible to link from within parts of the documentation that are not the \seealso section? For example, I would like to have something like: \arguments{ \item{correlation}{an optional \code{corStruct} object describing the within-group correlation structure; the available classes are given in

There are two ways to express file paths with the Windows environment: > a=file.choose() > a [1] "C:\\Documents and Settings\\rbaer\\Desktop\\_VNT_Test\\coordFocused 20k F5 0ng Ki8751 t20.txt" and >b= paste(getwd(),"/",dir()[1],sep="") >b [1] "C:/Documents and Settings/rbaer/Desktop/_VNT_Test/coordFocused 20k F5 0ng Ki8751 t20.txt" I have 2

I got the following: > library(foreign) > swal = read.spss("swallowing.sav", to.data.frame =TRUE) Warning message: In read.spss("swallowing.sav", to.data.frame = TRUE) : swallowing.sav: Unrecognized record type 7, subtype 21 encountered in system file > The bulk of the data seems to read in a usable form, but I'm curious about what might be getting lost

I'd like to compare two models which were fitted using gls, however I'm having trouble interpreting the results of gls. If any of you could offer me some advice, I'd greatly appreciate it. Short explanation of models: These two models have the same fixed-effects structure (two independent, linear effects), and differ only in that the second model includes a corExp structure for

On 6/22/2017 7:05 PM, Caroline wrote: > ##MODISTools example > library(MODISTools) > library(lubridate) > setwd('~/Documents/Modis data') > > #####MODISTools with buffalo data > > ###Read in data rename for easier coding > tbdata <- read.csv('~/Desktop/All TB data for EVI, NDVI.csv') Since this dataset is only on your desktop it cannot help us

Dear R-users, I hope you will have time to read me and I will try to be brief. I am also sorry for my poor english. I used gls function from the package nlme to correct two types of bias in my database. At first, because my replicates are spatially aggregated, I would like to fit a corStruct function like corLin, corSpher, corRatio, corExp or corGaus in my gls model, and simultaneously,

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)

I have been looking at some 'table' examples in Peter Dalgaard's ISwR book, and I am confused by how to get right justification of my table headings when I use the tables() command. Compare the following: # Produces right justfified column names caff.marital=matrix(c(652,1537,598,242,36,46,38,21,218,327,106,67),nrow=3,byrow=T)