[R] Interpreting and visualising lme results

Jim Lemon jim at bitwrit.com.au
Fri Oct 26 13:47:54 CEST 2012

On 10/26/2012 04:32 PM, Santini Silvana wrote:
> Dear R users,
> I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green).
> model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4)
> Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533 6.520094
> Random effects: Formula: ~XLA | Species Structure: General positive-definite, Log-Cholesky parametrization            StdDev       Corr  (Intercept) 1.944574e-01 (Intr)XLA         6.134158e-06 -0.884Residual    1.636428e-01
> Fixed effects: MOE ~ XLA                 Value  Std.Error DF   t-value p-value(Intercept) 3.0558697 0.15075939 32 20.269847  0.0000XLA         0.0000005 0.00000335 32  0.150811  0.8811 Correlation:     (Intr)XLA -0.861
> Standardized Within-Group Residuals:       Min         Q1        Med         Q3        Max -1.8354171 -0.4704322  0.1414749  0.5500273  1.5950338
> Number of Observations: 38Number of Groups: 5
> I have read that large correlation values such as,Correlation:     (Intr)XLA -0.861"reflect an ill-conditioned model", in addition XLA does not have an effect on the model p=0.88. These results are not logic when I look at my data and therefore I think I am missing something in the model? It would be very helpful if someone has some tips on this? In addition, I was wondering if somebody knows what is the best way to visualise this kind of data (nested data)?

Hi Santini,
I am currently illustrating the results of nested analyses using the 
barNest function from the plotrix package. The illustrations display 
nested frequencies, proportions or location parameters, but convey the 
fairly complex relationships in a way understandable to most readers.


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