Interpreting and visualising lme results
On Oct 25, 2012, at 10:32 PM, Santini Silvana wrote:
Dear R users, I have used the following function (in blue)
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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]]
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David Winsemius, MD Alameda, CA, USA