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)?
Thank you very much for any help and time.


	[[alternative HTML version deleted]]

