search for: remldev

...Block=rep(1:3,each=9)) Rice.lmer<-lmer(Yield ~ (1|Variety) + (1|Stand) + (1|Block) + (1|Variety:Stand), data = Rice) Result: Linear mixed model fit by REML Formula: Yield ~ (1 | Variety) + (1 | Stand) + (1 | Block) + (1 | Variety:Stand) Data: Rice AIC BIC logLik deviance REMLdev 96.25 104.0 -42.12 85.33 84.25 Random effects: Groups Name Variance Std.Dev. Variety:Stand (Intercept) 1.345679 1.16003 Block (Intercept) 0.000000 0.00000 Stand (Intercept) 0.888889 0.94281 Variety (Intercept) 0.024691 0.15714 Residual...

...hich are the real AIC and logLik values for the different models? Thanks for your help, Jonathan Williams Output:- > fit0=lmer(y~x1+x2+(1|id), data=datx); print(summary(fit0),corr=F) Linear mixed model fit by REML Formula: y ~ x1 + x2 + (1 | id) Data: datx AIC BIC logLik deviance REMLdev 87.34 104.7 -38.67 63.96 77.34 Random effects: Groups Name Variance Std.Dev. id (Intercept) 0.016314 0.12773 Residual 0.062786 0.25057 Number of obs: 240, groups: id, 120 Fixed effects: Estimate Std. Error t value (Intercept) 0.50376 0.05219...

...gt; >>>>> (fm1 ? ?<- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) >>>> Linear mixed model fit by REML >>>> Formula: Reaction ~ Days + (Days | Subject) >>>> ? Data: sleepstudy >>>> ?AIC ?BIC logLik deviance REMLdev >>>> ?1756 1775 -871.8 ? ? 1752 ? ?1744 >>>> Random effects: >>>> ?Groups ? Name ? ? ? ?Variance Std.Dev. Corr >>>> ?Subject ?(Intercept) 612.092 ?24.7405 >>>> ? ? ? ? ?Days ? ? ? ? 35.072 ? 5.9221 ?0.066 >>&g...

Dear R users, I am trying to fit an lmer model with only random effects which is giving me the following error: Error : length(f1) == length(f2) is not TRUE In addition: Warning messages: 1: In P1L55:family : numerical expression has 390 elements: only the first used 2: In P1L55:family : numerical expression has 390 elements: only the first used I am trying to extract variance components

...o I take out the intercept term? Or if this is not possible for the lmer function, is it possible using lme function in the "nlme" package? Thank you very much in advance. Hanna Linear mixed model fit by REML Formula: values ~ (1 | lot) Data: resamp AIC BIC logLik deviance REMLdev -14.21 -9.459 10.1 -23.88 -20.21 Random effects: Groups Name Variance Std.Dev. lot (Intercept) 0.036077 0.18994 Residual 0.017278 0.13144 Number of obs: 36, groups: lot, 10 Fixed effects: Estimate Std. Error t value (Intercept) 99.78693 0.06421 1...

...ry small (3.5803e-05)compared to stata's (see below 1.002). Any ideas why this should be happening please....? LMER MODEL summary(lmer(AnxietyScore ~ cc + (1|SetID), data=mydf)) Linear mixed model fit by REML Formula: AnxietyScore ~ cc + (1 | SetID) Data: mydf AIC BIC logLik deviance REMLdev 493.4 503.4 -242.7 486.6 485.4 Random effects: Groups Name Variance Std.Dev. SetID (Intercept) 1.2819e-09 3.5803e-05 Residual 1.3352e+01 3.6540e+00 Number of obs: 90, groups: SetID, 33 Fixed effects: Estimate Std. Error t value (Intercept) 3.1064 0...

...he variable SS, Value is the outcome measure, each subject has Value measured three times. I have used the following code: fit1<-lmer(Value~Group+(1|SS),data=smallDS) print(fit1) Linear mixed model fit by REML Formula: Value ~ Group + (1 | SS) Data: Dataset AIC BIC logLik deviance REMLdev 284.8 290.4 -138.4 291.4 276.8 Random effects: Groups Name Variance Std.Dev. SS (Intercept) 1038.0 32.218 Residual 552.7 23.510 Number of obs: 30, groups: SS, 10 Fixed effects: Estimate Std. Error t value (Intercept)...

...e t-value on group.minus.1 is 0.63 and so the intervention did not have a significant effect. Is that the correct way of interpreting this? Troy Linear mixed model fit by REML Formula: TC ~ group.minus.1 + Visit + (Visit | Study.Number) + (1 | Study.Number) Data: rawdf AIC BIC logLik deviance REMLdev 4676 4710 -2330 4670 4660 Random effects: Groups Name Variance Std.Dev. Corr Study.Number (Intercept) 857.440 29.2821 Visit 21.471 4.6337 -0.755 Study.Number (Intercept) 508.757 22.5556 Residual 333.710 18.2677 Number of obs: 494, groups: Study.Number, 140 Fixed effects: Estimate Std. Err...

...fects model to a set of longitudinal data over a set of individual subjects: (fm1 <- lmer(x ~ time + (time|ID),aa)) I quite often find that the correlation between the random effects is 1.0: Linear mixed model fit by REML Formula: x ~ time + (time | ID) Data: aa AIC BIC logLik deviance REMLdev 28574 28611 -14281 28561 28562 Random effects: Groups Name Variance Std.Dev. Corr ID (Intercept) 77.035 8.7770 time 10.817 3.2889 1.000 Residual 112.151 10.5901 Number of obs: 3539, groups: ID, 1000 Fixed effects: Estimate St...

...? ? ?Med ? ? ? ? Q3 ? ? ? ?Max?-1.9928118 -0.6586834 -0.1004301 ?0.6949714 ?2.0225381? Number of Observations: 60Number of Groups: 12? > lmer(root~fertilizer+(week|plant),data=dat)Linear mixed model fit by REML?Formula: root ~ fertilizer + (week | plant)?? ?Data: dat?? ?AIC BIC logLik deviance REMLdev?174.4 187 -81.21 ? ?159.7 ? 162.4Random effects:?Groups ? Name ? ? ? ?Variance ? Std.Dev. ? Corr ??plant ? ?(Intercept) 4.1416e-18 2.0351e-09 ? ? ??? ? ? ? ? week ? ? ? ?8.7452e-01 9.3516e-01 0.000??Residual ? ? ? ? ? ? 2.2457e-01 4.7389e-01 ? ? ??Number of obs: 60, groups: plant, 12 Fixed effects:...

...- And the full model summary looks like this.. Linear mixed model fit by maximum likelihood Formula: d15N ~ (AGECAT2 + Sex + Location1 + AGECAT2:Location1 + Sex:Location1 + AGECAT2:Sex + (1 | Year) + (1 | Location1/Socialgroup/Tattoo)) Data: nocubs AIC BIC logLik deviance REMLdev 1568 1670 -761.1 1522 1534 Random effects: Groups Name Variance Std.Dev. Tattoo:(Socialgroup:Location1) (Intercept) 0.35500 0.59582 Socialgroup:Location1 (Intercept) 0.35620 0.59682 Location1 (Intercept) 0.00000 0.0000...

...my model to hopefully illustrate my question. I'm happy to provide additional information/output if someone is interested in helping me with this problem. Thank you, Laura Linear mixed model fit by REML Formula: PTR ~ Test * Group + (1 | student) Data: ptr AIC BIC logLik deviance REMLdev -625.7 -559.8 323.9 -706.5 -647.7 Random effects: Groups Name Variance Std.Dev. student (Intercept) 0.0010119 0.03181 Residual 0.0457782 0.21396 Number of obs: 2952, groups: studentID, 20 Fixed effects: Estimate Std. Error t value (Intercept) 0.547...

...,"C","Control",..: 1 1 1 1 1 1 1 1 1 1 ... I fit a mixed model using both lmer() from lme4 package and lme() from nlme package: > lmer(score~trt+(1|id/eye),dat) Linear mixed model fit by REML Formula: score ~ trt + (1 | id/eye) Data: dat AIC BIC logLik deviance REMLdev 446.7 495.8 -212.4 430.9 424.7 Random effects: Groups Name Variance Std.Dev. eye:id (Intercept) 6.9208e+00 2.630742315798 id (Intercept) 1.4471e-16 0.000000012030 Residual 1.8750e-02 0.136930641909 Number of obs: 640, groups: eye:id, 160; id, 80 > summa...

...t won't tell me the p value. library(lme4) m<-lmer(enfa_mortality ~ guild_removal*enfa_removal +(1|block), data=com_summer) here is the result Linear mixed model fit by REML Formula: enfa_mortality ~ guild_removal * enfa_removal + (1 | block) Data: com_summer AIC BIC logLik deviance REMLdev 8.552 15.87 1.724 -16.95 -3.448 Random effects: Groups Name Variance Std.Dev. block (Intercept) 0.000000 0.00000 Residual 0.035380 0.18809 Number of obs: 25, groups: block, 5 Fixed effects: Estimate Std. Error t value (Intercept)...

Hello, I have a bar plot where I am already using colour to distinguish one set of samples from another. I would also like to highlight a few of these bars as ones that should be looked at in detail. I was thinking of using hatching, but I can't work out how or if you can have a background colour and hatching which is different between bars. Any suggestions on how I should do this? Thanks

...eme 2 13.161 6.5803 1.3255 The problems I have are: (1) How can I get the P-values? (2) How can I get the overall model statistic? Than I do: > summary(fm1) and get: Linear mixed model fit by maximum likelihood Formula: dbh ~ spec + scheme + (1 | Plot) Data: d AIC BIC logLik deviance REMLdev 147.2 157 -66.6 133.2 125.8 Random effects: Groups Name Variance Std.Dev. Plot (Intercept) 0.0000 0.0000 Residual 4.9644 2.2281 Number of obs: 30, groups: Plot, 2 Fixed effects: Estimate Std. Error t value (Intercept) 6.9074 0.9424 7.329 s...

...t; dat$Condicion2 <- + factor(rep(x= c("X","Y"))) > mod <- lmer( RT ~ Condicion1 * Condicion2 + (1|Subject), data=dat) > summary(mod) Linear mixed model fit by REML Formula: RT ~ Condicion1 * Condicion2 + (1 | Subject) Data: dat AIC BIC logLik deviance REMLdev 162.3 168.3 -75.15 175.7 150.3 Random effects: Groups Name Variance Std.Dev. Subject (Intercept) 101.59 10.079 Residual 405.85 20.146 Number of obs: 20, groups: Subject, 4 Fixed effects: Estimate Std. Error t value (Intercept)...

...ource) 1 426 3.754112 0.0533 summary(lmer(HD~1+(as.factor(Treatment)*as.factor(Source)|Family),data=regrexpdat)) Linear mixed model fit by REML Formula: HD ~ 1 + (as.factor(Treatment) * as.factor(Source) | Family) Data: regrexpdat AIC BIC logLik deviance REMLdev -103.5 -54.43 63.75 -132.5 -127.5 Random effects: Groups Name Variance Std.Dev. Corr Family (Intercept) 0.0113276 0.106431 as.factor(Treatment)...

...factor(rep(x= c("X","Y"))) > > mod <- lmer( RT ~ Condicion1 * Condicion2 + (1|Subject), data=dat) > > summary(mod) > Linear mixed model fit by REML > Formula: RT ~ Condicion1 * Condicion2 + (1 | Subject) > Data: dat > AIC BIC logLik deviance REMLdev > 162.3 168.3 -75.15 175.7 150.3 > Random effects: > Groups Name Variance Std.Dev. > Subject (Intercept) 101.59 10.079 > Residual 405.85 20.146 > Number of obs: 20, groups: Subject, 4 > > Fixed effects: > Estimate...

...> summary(temp.lme1) Linear mixed model fit by REML Formula: T.B ~ tarsus + wing + weight + factor(age) + factor(sex) + fat + minsunset + day1oct + day1oct.2 + minnight + ave.day + minnight.1 + T.A + ave.night.1 + (1 | ID) + (1 | sign) Data: bodytemp.df AIC BIC logLik deviance REMLdev 557.8 614 -260.9 441 521.8 Random effects: Groups Name Variance Std.Dev. ID (Intercept) 1.0399e-01 0.32247096 sign (Intercept) 6.2663e-08 0.00025033 Residual 8.0162e-01 0.89533134 Number of obs: 167, groups: ID, 124; sign, 2 Fixed effects:...