I am doing a model selection starting from the follwing full model (cma
):
vf1<-varIdent(form=~1|Region)
cf<-formula(rrG~plotsize+alt+baseT+baseP+baseN+EIV_R+EIV_F+Overstory+Overstory_diff+SCA+SCA_diff)
cma<- lme(cf,data=dat,random=~1|Region,weights=vf1,control=ctrl,na.action=na.exclude,method="ML")
Estimates are the following:
Fixed effects: list(cf)
Value Std.Error DF t-value p-value
(Intercept) -0.007018325 0.003443949 1561 -2.037871 0.0417
plotsize 0.009858282 0.002483949 1561 3.968794 0.0001
alt -0.000319573 0.002196082 1561 -0.145520 0.8843
baseT -0.001808871 0.003164477 33 -0.571618 0.5715
baseP 0.000613365 0.003659040 33 0.167630 0.8679
baseN -0.000388065 0.003721218 33 -0.104284 0.9176
EIV_R 0.006280127 0.000986477 1561 6.366215 0.0000
EIV_F -0.000965895 0.000704349 1561 -1.371331 0.1705
Overstory -0.000144179 0.001148155 1561 -0.125574 0.9001
Overstory_diff -0.002515170 0.000999187 1561 -2.517217 0.0119
SCA -0.001903248 0.001116551 1561 -1.704577 0.0885
SCA_diff -0.002666183 0.000763426 1561 -3.492391 0.0005
I remove the least significant term (baseN) and then compare the models:
cma1<-update(cma,~. -baseN)
anova(cma,cma1)
Which shows the following:
Model df AIC BIC logLik Test L.Ratio p-value
cma 1 50 -6873.897 -6604.822 3486.948
cma1 2 49 -6875.886 -6612.192 3486.943 1 vs 2 0.01090057 0.9168
I should I interpret that? The new model as lower AIC and BIC, so I should go for that one, however p-value is >0.05 so the data used in the models do not makes the two models significantly different, right? Does it mean that my model selection stops here and select the full model as the best one?