Basic question about interpretation of lme () result.
DeaR mixed effect model users I'm need some advice regarding interpretation of the lme () result. My question is possible too basic, but I hope someone could help me with advices (it may be valuable for other lme() new beginners). Respons: Speed Fixed effects: Fuel, CarMod (1,2&3), Driver (Old or Young), and Fuel*CarMod. Random effects: Place Questions regarding my model, se below: 1. Is it right to interpret that CarMod2 is significant different from CarMod1? 2. Is it right to interpret that the effect of Fuel is different in CarMod2 compared to CarMod1? 3. Is there a guideline for reporting lme() result? I'm uncertain whether to report this result as a table with only the estimates from the lme () or a table with only the anova (mod1)?
mod1 <- lme(Speed ~ Fuel + Car + Driver + Fuel*Car, random=~1|Place,data=test) summary(mod1)
Linear mixed-effects model fit by REML
Data: test
AIC BIC logLik
261.2013 275.6996 -121.6007
Random effects:
Formula: ~1 | Place
(Intercept) Residual
StdDev: 0.0003238738 5.013858
Fixed effects: Speed ~ Fuel + Car + Driver + Fuel * Car
Value Std.Error DF t-value p-value
(Intercept) -29.33479 12.743084 30 -2.302017 0.0285
Fuel 10.04684 1.408789 30 7.131542 0.0000
CarMod2 46.55593 14.192029 7 3.280428 0.0135
CarMod3 1.65157 18.247158 7 0.090511 0.9304
DriverYoung 26.65219 1.688643 30 15.783202 0.0000
Fuel:CarMod2 -5.53264 1.624159 30 -3.406464 0.0019
Fuel:CarMod3 -0.18452 2.010470 30 -0.091779 0.9275
Number of Observations: 44
Number of Groups: 10
anova(mod1)
numDF denDF F-value p-value (Intercept) 1 30 8487.520 <.0001 Fuel 1 30 340.661 <.0001 Car 2 7 6.283 0.0274 Driver 1 30 235.860 <.0001 Fuel:Car 2 30 8.655 0.0011 Best regards R.S. Cotter