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
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