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Interpreting and visualising lme results
3 messages · Santini Silvana, David Winsemius, Jim Lemon
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
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. Jim