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.
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