search for: corcar1

...ot;0.5"),k=6) + s(Week,by=as.numeric(Treatment=="5"),k=6) + s(Week,by=as.numeric(Treatment=="15"),k=6) + s(Week,by=as.numeric(Treatment=="50"),k=6) + s(Week,by=as.numeric(Treatment=="150"),k=6) + s(Treatment,k=6,fx=FALSE) + factor(Treatment), correlation=corCAR1(form=~Week|factor(Treatment),data=indices,family=gaussian) I seem to be having difficulties with the correlation structure. An initial error occurred because replicates were taken at the same time: /Error in Initialize.corCAR1(X[[2L]], ...) : Covariate must have unique values within groups for c...

...ons. Based on the help files, two separate threads from the archives, and the Pinheiro and Bates nlme 3.0 manual, I became confused about which of two ways to check for a first-order temporal autocorrelation: Chla.1b <- lme(Chla ~ Temp*Light* Time - Temp*Light, random = ~1 | TankID, corr = corCAR1(form = ~Time | TankID), method="ML") Chla.1c <- lme(Chla ~ Temp*Light* Time - Temp*Light, random = ~1 | TankID, corr = corCAR1(form = ~1 | TankID), method="ML") Comparing these fits with inspection of plot(ACF(chla.model1),alpha=0.05) suggests to me that there are problem...

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' ste...

...R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R> attach(Blackmoor) R> lme(log.exercise ~ I(age - 8)*group, + random = ~ I(age - 8) | subject, + correlation = corCAR1(form = ~ age-8|subject), + data=Blackmoor) Error in lme.formula(log.exercise ~ I(age - 8) * group, random = ~I(age - : nlminb problem, convergence error code = 1 message = iteration limit reached without convergence (9) This converged in the example that I am studying. H...

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

...rst fit with nlme using the mean of each group. Comparing several models, the best fit is: modm0c<-nlme(pds~Asym/(1+exp((xmid-age)/scal)), fixed=list(Asym~1,xmid~1,scal~1), random=Asym+xmid~1|nichoir,data=croispulm, start=list(fixed=c(10,5,2.2)), method="ML", corr=corCAR1() ) with pds = weight, age = mean age of each age group, nichoir = nestbox (a factor of 17 levels) Based on the empirical autocorrelation function of the normalised residuals drawn from this model one can acceptaly assume that the normalized residuals behave like uncorrelated noise. Thou...

...ModelFit <- lme(fixed = DepVar ~FixedVar1+FixedVar2, random = ~RandomVar1+RandomVar2 | Subject, na.action = na.omit, data = dataset, corr = corAR1()) I assume this can't be the right specification since it neglects the repeated measure aspect of the data, so I instead decided to employ the corCAR1 structure, i.e.-- ModelFit <- lme(fixed = DepVar ~FixedVar1+FixedVar2, random = ~RandomVar1+RandomVar2 | Subject, na.action = na.omit, data = dataset, corr = corCAR1(0.5, form = ~ Years | Subject)) Now perhaps neither correlation structure is the right one (probably a different discussion for...

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 ?corCl...

...b=1.5, cex.axis=1,lwd=2) lines(p.d$DaysPT,b$fit+b$se.fit*1.96,lty=2,lwd=1.5) lines(p.d$DaysPT,b$fit-b$se.fit*1.96,lty=2,lwd=1.5) points(DaysPT,Diff) However, when I add a correlation structure and/or a variance structure so that the model may look like:- gamm(Diff~s(DaysPT3)+AirToC,correlation=corCAR1(form=~DaysPT|Animal),weights=varPower(form=~DaysPT),method="REML") I get this message at the point of inputting the line "b<-predict.gam(g.m$gam,p.d,se=TRUE)" Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths dif...

Hi R users, The nlme library enabled several alternate error structures useful for longitudinal or repeated-measures data. For example, a continuous AR(1) process: model_2 = update(model_1, correlation = corCAR1(form = ~ time | subject)) Does anybody know if this is available in lme4? Thank you Ben -- View this message in context: http://www.nabble.com/Alternate-error-structures-in-lme4--tp24226643p24226643.html Sent from the R help mailing list archive at Nabble.com.

...odel by: update(model, corr=corCompSymm(~1|bas)) This is not significant. One of my variables measures area upstream of the sample. Area is a continuous variable, and there are some identical values of area. Can I model the dependence among residuals with in group (bas) and along area? Can the corCAR1 be used and the area used as a position variable? If so does the position variable need to be sorted when using the corCAR1 structure? Or could one use spatial classes and use area as one position and watershed as the other substituting numbers for the grouping variable? I have been trying, but...

...n 5 sessions within a field (n=12) with ten traps in one field. res2a <- lme(response~x+y+z+treatment),correlation = corARMA(form = ~ session|trapfield, p = 1, q = 0), random=~1|field, na.action=na.omit, data=plates, method="ML") res2a <- lme(response~x+y+z+treatment,correlation = corCAR1(form = ~ session|trapfield), random=~1|field, na.action=na.omit, data=plates, method="ML") Error: Incompatible formulas for groups in "random" and "correlation" What is the problem? thanks a lot! Yvonne -- View this message in context: http://r.789695.n4.nabble.com...

Heya I am fitting linear mixed effects model in R and want to assess the model fit (with Animal number as random factor; repeated measures for Animals): ts.model <- lme(LOG_FOC_MW ~ R_DN_SUM + ANIMAL + SEX+ YY, data = t.data, random = ~ 1 | ANIMAL, correlation=corCAR1(0.2, form = ~1 | ANIMAL ), method='ML', na.action=na.omit)). Is there a possability to easly compute an R-square from the output of the model summary? I would appreciate any hint. Best regards Lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of Aquatic...

Dear all, I am trying to fit a nonlinear model with a autocorrelation term, but everytime I type in the command, I got an error message from Winwows and R closes itself. The command line is as follows: mod1<-nlme(V~A*exp(-B*A.O)*Vac.t.1.,data,fixed=A+B~1,random=A+B~1|ORDINAL,+ correlation=corCAR1(0.3179,~A.O|ORDINAL,TRUE),start=c(A=1.2,B=0.2)) I have already fitted this model allowing Phi to vary while optimizing, and it was fine, but as soon as I try to keep it fixed (argument "TRUE"), I simply can't I don't get any error message from R, just a Windows error seying some...

Hi folks I have some data to which I've been fitting linear mixed effects models. I am currently using a lme model in the nlme package, with terms for random effects due to repeated measures on individuals and the corCAR1 serial correlation structure. However, there is some suggestion in the data (and from theory) that a breakpoint (change point) model may be more appropriate. Scott, Norman and Berger's lmeChgPt model seems to go some way to fitting the requirements for such a model but does not incorporate, as...

...inear regression to the data, but I keep having problems with NLME. I have defined my data as a groupedData object, and when I try to fit the model I get this error message: dat<-groupedData(V~VAC|ID,data=dat) attach(dat) mod<-nlme(V~A*exp(B*YEAR)*VAC, fixed=A+B~1, random=A+B~1, correlation=corCAR1(), start=c(A=1.2,B=0.2)) Error in getGroups.data.frame(dataMix, eval(parse(text = paste("~1", deparse(groups[[2]]), : Invalid formula for groups Do you have any clue of what?s happening? It?s the first time I fit a model like this in R, so the problem is probably pretty obvious...

Dear all, I run a gamm with following call : result <- try(gamm(values~ s( VM )+s( RH )+s( TT )+s( PP )+RF+weekend+s(day)+s(julday) ,correlation=corCAR1(form=~ day|month ),data=tmp) )" with mgcv version 1.6.2 No stress about the data, the error is not data-related. I get : Error in isS4(x) : object 'VM' not found What so? I did define the dataframe to be used, and the dataframe contains a variable VM : > str(tmp) 'data.fram...

...asurements for a developmental series of two capuchin monkey species. I'm interested in whether the rate of bone length scaling to body mass (i.e., growth) differs between species. My call to lme looks like this: model=lme(lghumlen ~ species + lgbmkg, random= ~ lgbmkg | id, correlation=corCAR1(), weights=varExp()) where lghumlen is the log of humerus length and lgbmkg is the log of body mass. If I ask for a summary of my model, I get this from the table of fixed effects: Fixed effects: lghumlen ~ species + lgbmkg Value Std.Error DF t-value p-value (Int...

Thankyou for your help with subsetting within a function. I have now tried to apply the same theory in the framework of an lme as follows: fit1.lme <- eval(substitute(lme(fixed=fixed, data=dataframe, random=random, correlation=corCAR1(form= corr), na.action=na.omit, subset=subset),list(subset=subs))) but I get the following error: Error in switch(mode(object), name = , numeric = , call = object, character = as.name(object), : [[ cannot be of mode logical I'm not sure why. Can anyone help me with this? kind regard...

...lculate the correlation of lag 1 over 17 replicates a<-0 for (i in 1:17) { c<-ts( cover[run==i] ) d<-acf( c, lag=1, plot=F) a<-a+d$acf[2] } a<-a/17 a #[1] 0.9021463 #mixed effects model model1<-lme(cover~t,random=~t|run, method="ML") model2<-update(model1,correlation=corCAR1(0.902,form=~t|run)) anova(model1,model2) But this just gives significance for a lag of 1, so I tried to find out the correlation at greater lags with arima to be able to use corARMA() as correlation structure: arima(cover[run==1],order=c(100,0,0)) #does not work: ?error in polyroot(z): polynomia...