search for: mldevianc

Displaying 20 results from an estimated 50 matches for "mldevianc".

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2008 Oct 16
1
lmer for two models followed by anova to compare the two models
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):...
2006 Oct 05
1
lmer BIC changes between output and anova
...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)...
2006 Oct 18
1
lmer- why do AIC, BIC, loglik change?
...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...
2008 Apr 04
1
lme4: How to specify nested factors, meaning of : and %in%
...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...
2006 Feb 10
1
Lmer with weights
...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....
2005 Sep 19
1
How to mimic pdMat of lme under lmer?
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
2006 Nov 21
1
lme4 model with no fixed effects?
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...
2005 Apr 16
1
How to get predictions, plots, etc. from lmer{lme4}
...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...
2006 Feb 04
1
Mixed models and missing p-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...
2006 Apr 20
2
Missing p-values using lmer()
...; 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...
2006 Jul 04
1
lmer print outs without 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?
2006 Jul 28
3
random effects with lmer() and lme(), three random factors
...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...
2008 Jul 25
1
glht after lmer with "$S4class-" and "missing model.matrix-" errors
...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:...
2007 May 14
2
lmer function
...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,...
2005 Jun 24
1
lme4 extracting individual variance components
...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...
2009 Oct 29
1
singular variance-covariance warning in lmer
...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...
2005 Oct 28
2
Random effect models
...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...
2007 Jun 25
1
conflict between lme4 and RMySQL packages (PR#9753)
...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...
2005 Jul 12
2
testing for significance in random-effect factors using lmer
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
2005 Sep 07
1
FW: Re: Doubt about nested aov output
...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...