search for: corexp

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 not the same...

...he 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: gls(IKAfox~an,correlation=corExp(2071,form=~x+y,nugget=1.22),data=renliev) but I get > gls(IKAfox~an,correlation=corExp(2071,form=~x+y,nugget=1.22),data=renliev) Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial" I understand that the 470 geographical coordi...

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 model using one correlation class or the other. I would like my model to account for both temporal and spatial autocorrelation simultaneously. The ?corClasses help...

...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 message is: Error in gls(y2 ~ x2, correlation = corExp(form = ~coord2[, 1] + coord2[, : false convergence (8) I don´t know what causes this error. My problem is that this error stops all the calculations during the for...

...s(100,2), f = factor(rep(letters[1:20],each=5))) library(MASS) ## without correlation structure: fine glmmPQL(z~1,random=~1|f,data=d,family="poisson") ## with correlation structure, ## but x and y not defined outside of 'd' glmmPQL(z~1,random=~1|f,data=d, correlation=corExp(form=~x+y), family="poisson") ## Error in eval(expr, envir, enclos) : object 'x' not found x = y = runif(10) glmmPQL(z~1,random=~1|f,data=d, correlation=corExp(form=~x+y), family="poisson") ## Error in ... ## variable lengths differ (found for...

...ith range=200 and nugget.effet of 0.3) in a lme model of this form: lme(ARBUS~YEAR, random=~1|IDSOUS). The structure of the data is "IDSOUS" "XMIN" "YMAX" "YEAR" "ARBUS" with 2 years of data and 5600 points for each year. I do: corstr<-corExp(value=200, form=~XMIN+YMAX|YEAR, nugget=0.3) with |year to calculate correlation of IDSOUS(plot) only within the same year since their positions did not change between the 2 years. Then I try to initialize the corExp vector: > corstr<-Initialize(corstr, data=cover) Erreur : impossible d...

...'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 spatial autocorrelation. (more detailed explanation of the models at end). Specific questions: 1. The second model estimates two additional parameters in the process of fitting the corSpatial object - the range and nugget of the spatial autocorrelation. Based on this, I would expect...

...ange tree data set. However, R crashes frequently VW> when using the gnls() function. With the following VW> simple example, I was able to reproduce the problem. VW> gnls.exp <- gnls(circumference ~ Asym/(1 + exp(-(age-xmid)/scal)) , VW> data = Orange, correlation = corExp(form = ~1 | Tree), VW> start = c(Asym=150, xmid=750, scal=300)) Yes, I can reproduce it (on a Linux (RHEL 5) 64bit server), using R 2.8.0 alpha : Use this for(i in 1:20)# for MM: ca. 8 to 10 times --> seg.fault try( gnls.exp <- gnls(circumference ~ Asym/(1 + exp(-(age-xmid)/scal)) ,...

  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

...ME package. Specifically I like to see the code for the functions coef, corMatrix, and intialize for any of the pre-defined correlation structures, and use this as a template to define a new correlation structure. So how do I see e.g. the code for the method initialize for the correlation structure corExp or corARMA? thank you in advance! Michael Jerosch-Herold PS: Oh, and if somebody could still send me example code for a user defined correlation structure I would much appreciate it, as my previous requests for help have not yielded any response.

Dear R-help, Can anyone explain the meaning of the warning, Singular precision matrix in level -1, block 1 ? Or how to track down where it comes from? More precisely, using the nlme package, I'm issued with the warning itt2 <- lme(lrna~rx.nrti+lbrna, random=~1|patid, cor=corExp(form=~days|patid,nugget=T), weights=varPower( form=~lbrna),data=rna3) Warning messages: 1: Singular precision matrix in level -1, block 1 2: Singular precision matrix in level -1, block 1 the output is: Linear mixed-effects model fit by REML Data: rna3 Log-restricted-likelihood: -4990.142 F...

Hello, I have been asked to perform a exponential correlation on a data set. Unfortunately, i did not find any simple example on how to do that with R. If the correlation is significant, one asked me to also find the thresold of the acceleration slope. Have any of you a little example or a good link to get me started? Best regards Blaise -- Mutt, c'est de la balle, quand ??a marche :-)

Hello, I am comparing 2 nonlinear models one with a correlation structure. I used the gnls command in the nlme package to fit model 1 and then the update(mod1,corr=corExp(form=~lat+lon,nugget=T) to fit the same model but with a correlation structure. I am comparing models using AIC, but I am getting results that don't seem to match. I get matching results for the model with no correlation structure, but non corresponding results for the model with a correlatio...

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

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. The link for the webpage is: http://www.ats.ucla.edu/stat/r/faq/spatial_regression.htm install.packages("nlme") library(nlme) spdata <- read.table("ht...

...ally correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 , family=binomial, correlation=corExp(1,form=~longitude+latitude)) This doesn't work because lmer says it needs a random effect in the model. And, apart from the spatial random effect that I want to capture by computing the correlation matrix, I have no other random effect. There must be something I do not understand here... I...

...lots of these (13000) cells which are classfied into ~2000 AREA's and ~750 STATE'S So my question is do AREA'S differ in the amount of grass they contain i.e does AREA A contain significantly more grass than AREA B? I have modelled this by area_grass <- gls(grass~AREA, correlation=corExp(form=~x+y), data = grassland I have set the contrasts to options(contrasts = c("contr.treatment", "contr.poly")) as there are no control groups. What i will get ( it is taking ages!) is AREA A: -0.12.... ** AREA B: 0.17....* AREA C.. So can i then say AREA A has s...

...; [13] "package:methods" "Autoloads" "package:base" > m.1 <- glmmPQL(fixed=cbind(nb_pc_02,I(nb_expr_02-nb_pc_02))~(PSDC99 +I(log(RFUCQ202))+PCCHOM99+sd_RFUCQ202+PCFEMCHOM)^2+langue, + random= ~ 1 | bidon, + correlation = corExp(form = ~ Longitude +Latitude), + family=quasibinomial, + na.action="na.exclude", + data=d.glmm) iteration 1 Erreur dans model.frame.default(formula = ~Longitude + Latitude + bidon les longueurs des variables diff?rent (trouv? pour ...

...e you aware of this issue, in case it is an error in gls (2) For my information, in case I have made an error, in the hope that one of you folks might be able to correct me. Thanks in advance for your time. The issue is in 2 parts. (A) I've used gls to fit a model with two fixed effects and a corExp object. By my count, this fitting process estimates 5 parameters: (Intercept), l10area, newx, range, and nugget. With 118 total df, there should be 118 - 5 = 113 residual df. However, the output from summary.gls reports 115 residual degrees of freedom. Is this an error in summary or gls, or is ther...

...ted a join count test which suggests that there is some spatial autocorrelation. Consequently I have used the following code: library(MASS) attach(Birds) Birds$x <- Birds$LONGITUDE Birds$y <- Birds$LATITUDE M <- glmmPQL(PRESENCE~ HYDROVAR1 + HYDROVAR2, random= ~ 1|fAVGNTS, correlation = corExp(form = ~ x + y), family = binomial(link = "logit"), data = Birds) The model seems to run fine. However, when I compare the results of this model and the residual spread against the same model but without the correlation function, there is absolutely no difference at all. I am somewhat...