search for: lme2

...ot;, "\\1.\\2", dat$amp)) ##### (2) Explorative plots ##### library(lattice) xyplot(resp ~ amp|id, dat, type=c("g","p","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"...

...wice. 8.0,9.2, 11.3,10.6, 9.8,11.9, 4.0,6.9, 9.8,10.1, 11.7,12.6, 10.3,9.4, 11.4,10.5, 7.9,8.1, 9.3,10.6, 13.0,11.7, 8.3,7.9, 9.5,9.8, 12.2,12.3, 8.6,10.5) aov1 <- aov(h1~task*subj) anova(aov1) # See note below. lme1 <- lme(h1~task,random=~1|subj) lme2 <- lme(h1~task,random=~1|subj/task) anova(lme1,lme2) # for interaction anova(lme2) # for effect of task ------- The anova gives very close to the correct F values for subj (1.68, according to Hays) and for the interaction (7.19), but the wrong F values for task, because it treats task as a rand...

...on 4 112366 28092 1.2742 0.2962 Location:Transect 5 121690 24338 1.1039 0.3736 Residuals 40 881875 22047 I have tried the following lme function to specify that Site is random: > lme1 <- lme(sp~Location, random=~1|Site, data=mavric) > lme2 <- lme(sp~Location, random=~1|Location/Site, data=mavric) > anova(lme1) numDF denDF F-value p-value (Intercept) 1 40 3.418077 0.0719 Location 4 5 1.152505 0.4294 This gives me the correct F-value for Location from MSLocation/MSLocation:Transect, but...

...5.216 23 3 27.50000 42.336 5 3 28.16205 51.264 24 3 34.69391 48.960 45 3 28.79778 46.368 368 3 26.18006 45.792 29 3 29.75208 45.216 78 3 25.28393 43.200 44 3 23.32825 44.640 # lme-model with "individual" as random factor > incub.lme2<-lme(egg.temp~kjday+treat,random=~1|ind,data=incub.df) Fixed effects: egg.temp ~ kjday + treat Value Std.Error DF t-value p-value (Intercept) 24.937897 6.662475 11 3.743038 0.0032 kjday 0.108143 0.152540 7 0.708945 0.5013 treat3 -1.506605 0.485336 7 -3.104...

...or(B/V), data = oats, qr = T) summary(oats.aov) # now lme oats.lme<-lme(Y ~ N + V, random = ~1 | B/V, data = oats) anova(oats.lme, type="m") # Ok! # aov:Y ~ N * V + Error(B/V) oats.aov2 <- aov(Y ~ N * V + Error(B/V), data = oats, qr = T) summary(oats.aov2) # now lme - my trial! oats.lme2<-lme(Y ~ N * V, random = ~1 | B/V, data = oats) anova(oats.lme2, type="m") # differences!!! (except of interaction term) My questions: 1) Is there a possibility to reproduce the result of aov with interaction using lme? 2) If not, which result of the above is the correct one for the o...

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank

...########################### # first fit a model with single fixed effects of condition and groups result.lme1<-lme(RT~CONDITION+GROUP,data= meandata,random=~1|SUBJECT, method="ML") plot(effet.lme) # residuals are goodlooking #then I try to add an interaction term in the model result.lme2<-lme(RT~CONDITION*GROUP,data= meandata,random=~1|SUBJECT, method="ML") #I then compare both models anova(result.lme1,result.lme2) #gives Model df AIC BIC logLik Test L.Ratio p-value effet.lme 1 8 3696.878 3726.822 -1840.439 effet.lme2 2 14 3705.8...

...ct) grd$pred1 = predict(grd.lme1,level=0) summary(grd.lme1) # How go force intercept = 0 ??? grd.lme0 = lme(newbone~t*treat-1,data=grd,random=~1|subject) grd$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) #------...

...frame(trt), lumley1) lumley1 <- transform(lumley1, trtpair = paste(Active, Control)) sigma <- rep(sqrt(.5), nrow=lumley1) library(nlme) lme1 <- lme(Y1 ~ trt.B + trt.C + trt.D + trt.E, random = ~ 1 | trtpair, data=lumley1, var = varConstPower(form=~sigma, fixed=list(power=1))) summary(lme1) lme2 <- lme(Y2 ~ trt.B + trt.C + trt.D + trt.E, random = ~ 1 | trtpair, data=lumley1), var = varConstPower(form=~sigma, fixed=list(power=1))) summary(lme2)

...ter way to analyse this data compared to the AOV model, since Genotype is a random 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 at...

...when I want to investigate the influence of the age.group, and the two conditions on the rt: my.lme <- lme(rt ~ age.group + angles * hands, data = my.data, random = ~ 1 |subject) then I think I would have to compare the model above with a more elaborated one, including more interactions: my.lme2 <- lme(rt ~ age.group * angles * hands, data = my.data, random = ~ 1 |subject) and comparing them by performing a likelhood-ratio test, yes? I think, if I would like to generalize the influence of the experimental conditions on the rt I should define angles and hands as a random effect, yes?...

...2,2,2,2,2)) R <- factor(c(1,1,2,2,3,3,1,1,2,2,3,3)) s <- c(51.43,51.28,50.93,50.75,50.47,50.83,51.91,52.43,52.26,52.33,51.58,51.23) DS <- data.frame(F,R,s) DS$F <- as.factor(DS$F) DS$R <- as.factor(DS$R) library(nlme) lme1 <- lme(data = DS,s ~ F,random = list(R = pdLogChol(~F))) lme2 <- lme(data = DS,s ~ F,random = list(R = pdDiag(~F))) summary(lme1) summary(lme2) library(lme4) lmer1 <- lmer(data = DS,s ~ F + (F|R)) lmer2 <- lmer(data = DS,s ~ F + (1|R) + (1|F)) summary(lmer1) summary(lmer2) confidentiality notice: The information contained in this e-mail is confid...

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 3 624.3...

Hi, I'm confused about how to specify random and fixed factors in an aov() term. I tried to reproduce a textbook example: One fixed factor (Game, 4 levels) and one random factor (Store, 12 levels), response is Points. The random factor Store is nested in Game. I tried > str(kh.df) `data.frame': 48 obs. of 4 variables: $ Subj : Factor w/ 48 levels

...5.216 23 3 27.50000 42.336 5 3 28.16205 51.264 24 3 34.69391 48.960 45 3 28.79778 46.368 368 3 26.18006 45.792 29 3 29.75208 45.216 78 3 25.28393 43.200 44 3 23.32825 44.640 # lme-model with "individual" as random factor > incub.lme2<-lme(egg.temp~kjday+treat,random=~1|ind,data=incub.df) Fixed effects: egg.temp ~ kjday + treat Value Std.Error DF t-value p-value (Intercept) 24.937897 6.662475 11 3.743038 0.0032 kjday 0.108143 0.152540 7 0.708945 0.5013 treat3 -1.506605 0.485336 7 -3.10...

Hi, I applied a linear mixed effect model in my data using the nlme package. lme2<-lme(distance~temperature*condition, random=~+1|trial, data) and then anova. I want to ask if it is posible to get the least squares means for the interaction effect and the corresponding 95%ci. And then plot this values. Thank you Panagiotis -- View this message in context: http://r.789695....

...1 4 0.139445 0.7278 time 2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0....

...1 4 0.139445 0.7278 time 2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0.0...

...1 4 0.139445 0.7278 time 2 8 0.012365 0.9877 treat:sex 1 4 0.014175 0.9110 treat:time 2 8 0.120538 0.8880 sex:time 2 8 0.304878 0.7454 treat:sex:time 2 8 0.391012 0.6886 #### using y as dependable variable xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx) xx.aov2<-aov(y~treat*sex*time+Error(subject),xx) summary(xx.aov2) Error: subject Df Sum Sq Mean Sq F value Pr(>F) treat 1 0.147376 0.147376 2.0665 0.2239 sex 1 0.000474 0.000474 0.0067 0.9389 treat:sex 1 0.006154 0.0...

...ghness + DivBoulders , data= myData ) # random intercept and slope #Error in getGroups.data.frame(dataMix, groups) : # Invalid formula for groups # if I only use one random variable I have: #Error in chol.default((value + t(value))/2) : #the 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= ~ Rough...