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Dear Colleagues, I run this model: mod1 <- lmer(x~category+subcomp+category*subcomp+(1|id),data=impchiefsrm) obtain this summary result: Linear mixed-effects model fit by REML Formula: x ~ category + subcomp + category * subcomp + (1 | id) Data: impchiefsrm AIC BIC logLik MLdeviance REMLdeviance 4102 4670 -1954 3665 3908 Random effects: Groups Name Variance Std.Dev. id (Intercept) 0.11562 0.34003 Residual 0.22765 0.47713 number of obs: 2568, groups: id, 107 run this model (only difference is I've removed the interaction term):...

...ut assocaited with the anova is right!). thank you, darren > unconditional<-lmer(log50 ~ 1 + (1 | Stream:Site) + (1|Stream), data) > summary(unconditional) Linear mixed-effects model fit by REML Formula: log50 ~ 1 + (1 | Stream:Site) + (1 | Stream) Data: data AIC BIC logLik MLdeviance REMLdeviance -138.8 -132.8 72.42 -150.4 -144.8 > nosection<-lmer(log50 ~ 1 + meanlogATS + residuallogATS + (1 | Stream:Site) + (1|Stream), data) > summary(nosection) Linear mixed-effects model fit by REML Formula: log50 ~ 1 + meanlogATS + residuallogATS + (1 | Stream:Site)...

...Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days + (Days|Subject), sleepstudy) summary(fm2) anova(fm1, fm2) Sample output: > summary(fm1) Linear mixed-effects model fit by REML Formula: Reaction ~ Days + (1 | Subject) Data: sleepstudy AIC BIC logLik MLdeviance REMLdeviance 1792 1802 -893.2 1794 1786 > summary(fm2) Linear mixed-effects model fit by REML Formula: Reaction ~ Days + (Days | Subject) Data: sleepstudy AIC BIC logLik MLdeviance REMLdeviance 1754 1770 -871.8 1752 1744 > anova(fm1, fm2) Data: sleeps...

...lease assume that rats are nested within treatments, because that corresponds to the case I actually want to analyze. > (m1<-lmer(Glycogen~Treatment+(1|Treatment/Rat/Liver))) Linear mixed-effects model fit by REML Formula: Glycogen ~ Treatment + (1 | Treatment/Rat/Liver) AIC BIC logLik MLdeviance REMLdeviance 231.6 241.1 -109.8 234.9 219.6 Random effects: Groups Name Variance Std.Dev. Liver:(Rat:Treatment) (Intercept) 14.1617 3.7632 Rat:Treatment (Intercept) 36.0843 6.0070 Treatment (Intercept) 4.7039 2.1689 Residual...

...much! > library("R2WinBUGS") > data(schools) > schools > attach(schools) > > ## Fit simple model without "weights" > lmer(estimate ~ 1 + (1 | school)) Linear mixed-effects model fit by REML Formula: estimate ~ 1 + (1 | school) AIC BIC logLik MLdeviance REMLdeviance 58.882 59.041 -27.441 59.278 54.882 Random effects: Groups Name Variance Std.Dev. school (Intercept) 80.4 8.97 Residual 30.1 5.49 # of obs: 8, groups: school, 8 Fixed effects: Estimate Std. Error t value (Intercept) 8....

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Crossed random effects: > lmer( y ~ (1 | i1) + (1|i2) ,data=dta) Linear mixed-effects model fit by REML Formula: y ~ (1 | i1) + (1 | i2) Data: dta AIC BIC logLik MLdeviance REMLdeviance 91.18 94.84 -42.59 85.2 85.18 Random effects: Groups Name Variance Std.Dev. i1 (Intercept) 0.068224 0.26120 i2 (Intercept) 0.463112 0.68052 Residual 1.469250 1.21213 number of obs: 25, groups: i1, 5; i2, 5 Fix...

...4.5 12 115 6 7 145 20 1.00 0.0 73 65 0 2 147 20 1.00 3.0 63 72 2 3 etc. The output: > summary(exp1B.both.cont.lmer) Linear mixed-effects model fit by maximum likelihood Formula: cbind(b, a) ~ ba + amplitude + (1 | sub) Data: exp1B AIC BIC logLik MLdeviance REMLdeviance 768.0086 783.2579 -379.0043 758.0086 766.3104 Random effects: Groups Name Variance Std.Dev. sub (Intercept) 0.18787 0.43344 Residual 6.36355 2.52261 # of obs: 156, groups: sub, 13 Fixed effects: Estimate Std. Error DF t value...

Dear R-users, I computed a simple mixed models which was: mod<-lmer(nb ~ site + (1|patelle),tr) The output was: Linear mixed-effects model fit by REML Formula: nb ~ site + (1 | patelle) Data: tr AIC BIC logLik MLdeviance REMLdeviance 1157.437 1168.686 -574.7184 1164.523 1149.437 Random effects: Groups Name Variance Std.Dev. patelle (Intercept) 34.995 5.9157 Residual 744.736 27.2899 # of obs: 123, groups: patelle, 33 Fixed effects: Estimate Std. Error t value (I...

...; library(lme4) Loading required package: Matrix Loading required package: lattice > reml.res <- lmer(UNDS~SUCCESSMN+(1|BIRD), dive) > summary(reml.res) Linear mixed-effects model fit by REML Formula: UNDS ~ SUCCESSMN + (1 | BIRD) Data: dive AIC BIC logLik MLdeviance REMLdeviance 60032.37 60053.8 -30013.19 60031.9 60026.37 Random effects: Groups Name Variance Std.Dev. BIRD (Intercept) 4.4504 2.1096 Residual 36.4240 6.0352 number of obs: 9324, groups: BIRD, 12 Fixed effects: Estimate Std. Error t...

Hi, I have been having a tedious issue with lmer models with lots of factors and lots of levels. In order to get the basic information at the beginning of the print out I also have to generate these enormous tables as well. Is there a method command to leave off all of the effects and correlations? Or, do I have to go to string commands?

...Matrix package: fm <- lmer(y ~ (1 | Sample) + (1 | Operator) + (1|Operator:Run), data=x) summary(fm) Linear mixed-effects model fit by REML Formula: H.I.Index ~ (1 | Sample.Name) + (1 | Operator) + (1 | Operator:Run) Data: x AIC BIC logLik MLdeviance REMLdeviance 96.73522 109.0108 -44.36761 90.80064 88.73522 Random effects: Groups Name Variance Std.Dev. Operator:Run (Intercept) 4.2718e-11 6.5359e-06 Operator (Intercept) 5.4821e-04 2.3414e-02 Sample (Intercept) 5.0352e+00 2.2439e+00 Residual...

...r(length~comp+(meas|box_id),data=sv.growth) Warning message: In .local(x, ..., value) : Estimated variance-covariance for factor 'box_id' is singular > summary(model.sv) Linear mixed-effects model fit by REML Formula: length ~ comp + (meas | box_id) Data: sv.growth AIC BIC logLik MLdeviance REMLdeviance 1587 1606 -786.4 1605 1573 Random effects: Groups Name Variance Std.Dev. Corr box_id (Intercept) 466698.1 683.153 meas 230733.7 480.347 -1.000 Residual 9138.3 95.595 number of obs: 120, groups: box_id, 40 Fixed effects:...

...d this model hive the right results for the variance components? mod_3_f <- lmer(SCORE ~ GENDER + (1 |ID ) + (1 | TERM) + (1 | SUBJECT) , Dataset) Linear mixed-effects model fit by REML Formula: SCORE ~ GENDER + (1 | ID) + (1 | TERM) + (1 | SUBJECT) Data: Dataset AIC BIC logLik MLdeviance REMLdeviance 247882 247926 -123936 247871 247872 Random effects: Groups Name Variance Std.Dev. ID (Intercept) 5.97288 2.44395 TERM (Intercept) 5.10307 2.25900 SUBJECT (Intercept) 0.25943 0.50934 Residual 4.41673 2.10160 number of obs: 53978,...

...ur help! Thomas My model: > m1<- lmer(y ~ trtt + (trtt-1|group3) + (trtt-1|group2) + (trtt-1|group1), d1) > m1 Linear mixed-effects model fit by REML Formula: y ~ trtt + (trtt - 1 | group3) + (trtt - 1 | group2) + (trtt - 1 | group1) Data: d1 AIC BIC logLik MLdeviance REMLdeviance 1819.454 2003.915 -874.7269 1736.421 1749.454 Random effects: Groups Name Variance Std.Dev. Corr group1 trtt1/TR1 0.115094 0.33926 trtt1/TR2 0.338576 0.58187 0.177 trtt2/TR1 0.141726 0.37647 -0.002 -0.007...

...ssage: Estimated variance-covariance for factor 'female' is singular in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = 1.49011611938477e-08, summary(model) Linear mixed-effects model fit by REML Formula: int.length ~ mean.sst + (mean.sst | female) AIC BIC logLik MLdeviance REMLdeviance 155.4 164.5 -72.7 142.8 145.4 Random effects: Groups Name Variance Std.Dev. Corr female (Intercept) 6.8459e-10 2.6165e-05 mean.sst 6.8169e-10 2.6109e-05 -0.065 Residual 1.3634e+00 1.1676e+00 number of obs: 46, groups: female, 18...

...e4) > lme1 <- lmer(Rendement ~ (1|Pollinisateur) + (1|Lignee) + (1|Pollinisateur:Lignee), data = mca2)) > summary(lme1) Linear mixed-effects model fit by REML Formula: Rendement ~ (1 | Pollinisateur) + (1 | Lignee) + (1 | Pollinisateur:Lignee) Data: mca2 AIC BIC logLik MLdeviance REMLdeviance 105.3845 118.4104 -47.69227 94.35162 95.38453 Random effects: Groups Name Variance Std.Dev. Pollinisateur:Lignee (Intercept) 0.033892 0.18410 Pollinisateur (Intercept) 0.118664 0.34448 Lignee (Intercept) 0.218183 0.46710 Resi...

...ice library(RMySQL)> library(RMySQL) Loading required package: DBI > data(sleepstudy) > fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) > summary(fm1) Linear mixed-effects model fit by REML Formula: Reaction ~ Days + (Days | Subject) Data: sleepstudy AIC BIC logLik MLdeviance REMLdeviance 1754 1770 -871.8 1752 1744 Random effects: Groups Name Variance Std.Dev. Corr Subject (Intercept) 610.835 24.7151 Days 35.056 5.9208 0.067 Residual 655.066 25.5943 number of obs: 180, groups: Subject, 18...

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

...ly Fixed Effect,with Rat and the interaction Rat:Liver as random effects then-- > model.lmer<-lmer(Glycogen~Treatment+(1|Rat)+(1|Rat:Liver)) > summary(model.lmer) Linear mixed-effects model fit by REML Formula: Glycogen ~ Treatment + (1 | Rat) + (1 | Rat:Liver) AIC BIC logLik MLdeviance REMLdeviance 239.095 248.5961 -113.5475 238.5439 227.095 Random effects: Groups Name Variance Std.Dev. Rat:Liver (Intercept) 2.1238e-08 0.00014573 Rat (Intercept) 2.0609e+01 4.53976242 Residual 4.2476e+01 6.51733769 # of obs: 36, groups: Rat:Liver, 6; Rat...