Dear Thierry,
Thanks a lot for your reply. Yes, I have used dB1c also in the random effect.
I have just tried to scale dB1 but I have still the same problem. However, it is possible that I am not doing things well, I am not a statistician and moreover I am just discovering R...
You say that I should provide more information so here is the summary of my data for the two columns I am using in this model:
Species MCs dB1
327 :43 Min. 40.05 Min. :1.050
135 :35 1st Qu. 72.53 1st Qu. :1.400
307 :24 Median 89.11 Median :1.560
146 :23 Mean 99.56 Mean :1.671
328 :23 3rd Qu. 116.23 3rd Qu. :1.840
341 :22 Max. 351.49 Max. :4.220
(Other):2051
How I centered and scaled dB1:
dB1c<-scale(data$dB1,center=TRUE)
dB1s<-scale(data$dB1,center=FALSE, scale=TRUE)
summary of the model without centering or scaling dB1:
Linear mixed model fit by maximum likelihood t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: MCs ~ dB1 + (1 + dB1 | Species)
Data: data
AIC BIC logLik deviance df.resid
10720.4 10754.7 -5354.2 10708.4 2215
Scaled residuals:
Min 1Q Median 3Q Max
-8.7305 -0.3491 0.0651 0.4508 6.6068
Random effects:
Groups Name Variance Std.Dev. Corr
Species (Intercept) 21.48 4.635
dB1 11.25 3.355 -1.00
Residual 6.19 2.488
Number of obs: 2221, groups: Species, 598
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -64.2618 0.4249 273.4200 -151.2 <2e-16 ***
dB1 98.0060 0.2800 271.1900 350.0 <2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr)
dB1 -0.987
summary of the centered model m4c:
Linear mixed model fit by maximum likelihood t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: MCs ~ dB1c + (1 + dB1c | Species)
Data: data
AIC BIC logLik deviance df.resid
10720.4 10754.7 -5354.2 10708.4 2215
Scaled residuals:
Min 1Q Median 3Q Max
-8.7305 -0.3491 0.0651 0.4508 6.6068
Random effects:
Groups Name Variance Std.Dev. Corr
Species (Intercept) 1.109 1.053
dB1c 1.763 1.328 0.94
Residual 6.190 2.488
Number of obs: 2221, groups: Species, 598
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 99.54109 0.08466 290.67000 1176 <2e-16 ***
dB1c 38.78838 0.11081 271.18000 350 <2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr)
dB1c 0.575
and summary of the scaled model m4s:
Linear mixed model fit by maximum likelihood t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: MCs ~ dB1s + (1 + dB1s | Species)
Data: data
AIC BIC logLik deviance df.resid
10720.4 10754.7 -5354.2 10708.4 2215
Scaled residuals:
Min 1Q Median 3Q Max
-8.7305 -0.3491 0.0651 0.4508 6.6068
Random effects:
Groups Name Variance Std.Dev. Corr
Species (Intercept) 21.48 4.635
dB1s 33.22 5.763 -1.00
Residual 6.19 2.488
Number of obs: 2221, groups: Species, 598
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -64.2618 0.4249 273.4100 -151.2 <2e-16 ***
dB1s 168.3687 0.4810 271.1800 350.0 <2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr)
dB1s -0.987
Thanks in advance for your help!
Best regards
Jana
-----Message d'origine-----
De : Thierry Onkelinx [mailto:thierry.onkelinx at inbo.be] Envoy? : mardi
13 f?vrier 2018 10:26 ? : Jana Dlouha <jana.dlouha at inra.fr> Cc :
r-sig-mixed-models at r-project.org Objet : Re: [R-sig-ME] correlation
between random effects
Dear Jana,
I assume that you uses the centered dB1c both in the random and the fixed effects? Another thing you can try is to scale dB1c. Using sensible units is often sufficient. Don't use large units (e.g.
kilometers) when you are measuring small things (e.g. millimeters).
You'll need to provide more information when you need more feedback.
At least the summary of the data and the model.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN
BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie
& Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be
//////////////////////////////////////////////////////////////////////
///////////////////// To call in the statistician after the experiment
is done may be no more than asking him to perform a post-mortem
examination: he may be able to say what the experiment died of. ~ Sir
Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger
Brinner The combination of some data and an aching desire for an
answer does not ensure that a reasonable answer can be extracted from
a given body of data. ~ John Tukey
//////////////////////////////////////////////////////////////////////
/////////////////////
2018-02-13 10:00 GMT+01:00 Jana Dlouha <jana.dlouha at inra.fr>:
Hi all,
I have a problem with a correlation between random effects. I have tested several models on my data:
m0<-lm(MCs~ dB1, data)
m1<- lmer(MCs~ dB1 + (1|Species), data, REML=FALSE)
m2 <- lmer(MCs~ dB1 + (-1+dB1|Species), data, REML=FALSE)
m3<- lmer(MCs~ dB1 + (1|Species)+(0+dB1|Species), data, REML=FALSE)
m4<- lmer(MCs ~ dB1 + (1+dB1 |Species), data,REML=FALSE)
and when I compare the AIC criterion, the lowest one is for the model m4:
m0 m1 m2 m3 m4
11086.51 10948.72 10828.75 10830.75 10720.43
However, in the summary I see that there is a strong correlation between random effects and associated variances are huge:
Random effects:
Groups Name Variance Std.Dev. Corr
Species (Intercept) 21.48 4.635
dB1 11.25 3.355 -1.00
Residual 6.19 2.488
Number of obs: 2221, groups: Species, 598
For m3, random effect associated with intercept has very low variance and residual variance is only a bit higher:
Random effects:
Groups Name Variance Std.Dev.
Species (Intercept) 3.419e-14 1.849e-07
Species.1 dB1 7.968e-01 8.927e-01
Residual 6.327e+00 2.515e+00
Number of obs: 2221, groups: Species, 598
I am tempted to take into account only the randon effect associated with the slope however I don't know if i can do this considering that the AIC is not the lowest one for this model and how to justify it in my paper?
By the way, I don't really understand why the variances associated with the random effects change so much.
I have tried to center the regressor dB1 which removed the correlation between fixed effects and changed the sign of correlation but random effects remain strongly correlated and variances large:
Random effects:
Groups Name Variance Std.Dev. Corr
Species (Intercept) 1.109 1.053
dB1c 11.255 3.355 0.94
Residual 6.190 2.488
Number of obs: 2221, groups: Species, 598
Could you please give me some hint to solve my problem? Thanks a lot
in advance
Jana
[[alternative HTML version deleted]]