On Oct 25, 2012, at 10:32 PM, Santini Silvana wrote: > Dear R users, > I have used the following function (in blue)
No, we do not do "in blue" here. This is a monochrome mailing list. > 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]] We also do not do HTML. This message is mangled. -- David Winsemius, MD Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
