search for: lme3

...AIC BIC logLik Test L.Ratio p-value effet.lme 1 8 3696.878 3726.822 -1840.439 effet.lme2 2 14 3705.821 3758.223 -1838.910 1 vs 2 3.056792 0.8017 # Thus I only use the 1st model without interaction term # try another model adding an acoustic parameter as a fixed effect result.lme3<-lme(RT~CONDITION+GROUP+ACOUSTQ,data= meandata,random=~1|SUBJECT, method="ML") anova(result.lme1,result.lme3) # gives : Model df AIC BIC logLik Test L.Ratio p-value effet.lme 1 8 3696.878 3726.822 -1840.439 effet.lme3 2 9 3668.755 3702.442 -1825....

...rd,random=~1|subject) grd$pred0 = predict(grd.lme0,level=0) summary(grd.lme0) # Gives true, all.equal(grd$pred1,grd$pred0) # Everything as expected without treat grd.lme2 = lme(newbone~t,data=grd,random=~1|subject) grd$pred2 = predict(grd.lme2,level=0) summary(grd.lme2) # Forced intercept = 0 grd.lme3 = lme(newbone~t-1,data=grd,random=~1|subject) grd$pred3 = predict(grd.lme3,level=0) summary(grd.lme3) # As expected: not equal all.equal(grd$pred2,grd$pred3) #------------------------------------------------------------------- R version 2.9.0 Under development (unstable) (2009-03-13 r48127) i386-...

...leading minor of order 2 is not positive definite lme2 <- lme( Biomass ~ fReserve , random = ~1 | Roughness + DivBoulders ,data=myData) #random intercept #Error in getGroups.data.frame(dataMix, groups) : # Invalid formula for groups # if I only use one random variable my result is fine! lme3 <- lme (Biomass ~ fReserve , random= ~ Roughness + DivBoulders | fReserve , data= myData ) # from help (lme) summary (lme3) # I have a result. Is the model correct for what I want? # BUT my fixed effect (reserve) does not have a p-value due to zero degrees of freedom. However in glm1 it...

...,"smooth")) ##### (3) MIXED-EFFECTS REGRESSION MODELS ##### library(nlme) lme1 <- lme(resp ~ amp, dat, ~1|id, method="ML") lme2 <- lme(resp ~ amp + I(amp^2), dat, ~1|id, method="ML") lme2a <- lme(resp ~ amp + I(amp^2), dat, ~amp|id, method="ML") lme3 <- lme(resp ~ amp + I(amp^2) + I(amp^3), dat, ~1|id, method="ML") lme5 <- lme(resp ~ factor(amp), dat, ~1|id, method="ML") anova(lme1, lme2) anova(lme2, lme3, lme5) summary(lme2) ##### (4) observed vs. predicted data ##### plot(augPred(lme2, ~amp, level=0:1)) xvar...

...ndom factor. However, I obtain the same parameter estimate and p value for Age. Please can someone tell me why? 2: When using LME, when I am after a p value for the covariate Age, is it better to do the following; Model.lme2 <- lme (Weight~Age, random=~ 1|Genotype, method="ML") Model.lme3 <- lme (Weight~1, random=~ 1|Genotype, method="ML") Anova(Model.lme2, Model.lme3) Giving likelihood ratio=0.102, with p=0.749, which is slightly different to the p values of 0.758 above. Thanks for your attention, Martin. Martin Hoyle, School of Life and Environmental Sciences, Un...

Hi List I'm running a series of mixed models using lme, and I wonder if there is a way to sort them by AIC prior to testing using anova (lme1,lme2,lme3,....lme7) other than by hand. My current output looks like this. anova (lme.T97NULL.ml,lme.T97FULL.ml,lme.T97NOINT.ml,lme.T972way.ml,lme.T97fc. ml, lme.T97ns.ml, lme.T97min.ml) Model df AIC BIC logLik Test L.Ratio p-value lme.T97NULL.ml 1 3 624.3053 6...

Dear all, Using the data set and code below, I am interested in modelling how egg temperature (egg.temp) is related to energy expenditure (kjday) and clutch size (treat) in incubating birds using the lme-function. I wish to generate the F-distribution for my model, and have tried to do so using the anova()-function. However, in the resulting anova-table, the parameter kjday has gone from being

...15363.96 88.42 7 M 52 Undergraduate PMDB ... =>Questions: 1) I''ve found that the random effects of the ''state'' level have a very low standard deviation. >depfed.lme3<- lme(Votes~Sex, data=depfed.frm, random=~1|State/Party) >summary (depfed.lme3) (...)Random effects: Formula: ~1 | State (Intercept) StdDev: 0.0001089504 This suggests that there is no significant advantage on using ''state'' alone as a grouping factor, I su...

...s4 0.5m august NA 70 7 F s4 1.0m august NA 71 7 F s4 2.5m august 10.27613353 72 7 F s4 5.0m august 7.34681883 > lme1 = lme(y ~ M*D*Time, random=~1|Block/M/D/Time, data=PhotoRed, na.action=na.omit) > lme2 = update(lme1, correlation=corAR1()) > lme3 = update(lme1, correlation=corCompSymm()) Error in corMatrix.corCompSymm(object) : NA/NaN/Inf in foreign function call (arg 1) > cs = corCompSymm(value=0.5,form=~1|TreeID) > lme3 = update(lme1, correlation=cs) Error in lme.formula(fixed = y ~ M * D * Time, data = PhotoRed, random = ~1 | :...

...int count stations within a stand (~150 stations) visited twice a year and with multiple observers. I am using a linear mixed effects model (lme4) that includes year as a fixed effect and observer, station nested within stand (to account for spatial auto-correlation) and visit as random variables: lme3<-lmer(Abundance ~ Year + (1|Site/Station)+ (1|Observer)+ (1|Visit), data=AMRE, family = poisson(link=log)) To test the model, I simulated a data set (Poisson distribution) with a 50% decline over 50 years (with a mean abundance at year 1 of 10 individuals of species X). The parameter estimates...

..."contrast" or "estimate" or "lsmeans" in SAS). I have now found out that the vcov-function in the lme4-package could be of great help for me, but I do not know if I can use lme4 when working with S-PLUS 6.1, or is it only for R. When I write >library(MASS) I find lme3, where there is a vcov-function, but if I understand it correctly it works with lm and not with lme... If I can use lme4 with s-plus 6.1, what shall I do in order to install it on my computer? I am grateful for all help I can get? best regards Anna Persson

in lmList ,lmList.formula and lmList.groupedData functions in help pages there is an argument called "groups" that doesn't exist in the code of the functions. <<insert bug report here>> --please do not edit the information below-- Version: platform = i686-unknown-linux arch = i686 os = linux system = i686, linux status = Patched (unreleased snapshot) status.rev

Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See ?library. As for the appropriateness of your modeling, you should do what David already suggested and post to the r-sig-mixed-models list instead. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." --...

...e(resp~fact1*fact2, random=~1|subj/fact1/fact2) But this is not a correct model because it assumes the factors to be hierarchically ordered, which they are not. Another alternative is to model the random effect using a matrix, as seen in "[R] lme and mixed effects" on this list. > lme3 <- (resp~fact1*fact2, random=list(subj=pdIdent(form=~fact1-1), subj=~1, fact2=~1) This provides 'correct? degrees of freedom for fact1, but not for the other effects and I must confess that I don't understand this use of matrices, I?m not a statistician. My questions thus come dow...

...day, November 15, 2017 10:44 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See ?library. As for the appropriateness of your modeling, you should do what David already suggested and post to the r-sig-mixed-models list instead. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." --...

Estimados Escribo para consultar sobre el uso de modelos mixtos anidados. Los datos que estoy analizando provienen de censos de malezas en cuatro tipos de paisajes de la región pampeana, en los que seleccioné al azar igual número de lotes agrícolas cultivados con tres cultivos (maíz, soja y trigo-soja). En cada lote censé el número de especies de malezas en tres posiciones: el alambrado, el borde

...;> But this is not a correct model because it assumes the factors to be >> hierarchically ordered, which they are not. >> >> Another alternative is to model the random effect using a matrix, as >> seen in "[R] lme and mixed effects" on this list. >> > lme3 <- (resp~fact1*fact2, random=list(subj=pdIdent(form=~fact1-1), >> subj=~1, fact2=~1) >> >> This provides 'correct? degrees of freedom for fact1, but not for the >> other effects and I must confess that I don't understand this use of >> matrices, I?m not...