[R] DF and intercept term meaning for mixed (lme) models
Dylan Beaudette
dylan.beaudette at gmail.com
Wed Jul 25 20:28:42 CEST 2007
Hi,
I am using the lme package to fit mixed effects models to a set of data.
I am having a difficult time understanding the *meaning* of the numDF (degrees
of freedom in the numerator), denDF (DF in the denomenator), as well as the
Intercept term in the output.
For example:
I have a groupedData object called 'Soil', and am fitting an lme model as
follows:
## fit a simple model
# errors partitioned among replicates
fit1 <- lme(
log(ksat) ~ log(conc) + ordered(sar) + soil_id ,
random = ~ 1 | rep,
data=Soil
)
## check significance of model terms
anova(fit1)
numDF denDF F-value p-value
(Intercept) 1 1253 64313.21 <.0001
log(conc) 1 597 173.34 <.0001
ordered(sar) 2 597 13.87 <.0001
soil_id 29 597 54.92 <.0001
I am pretty sure that I am interpreting the p-values for the predictor terms
to mean that these terms contribute significantly to the variation in the
response variable, (?) . I am not sure what the significance of the Intercept
term really means. Does it have something to do with the significance of the
random effects in the model?
Also, from a practical standpoint, how can I best describe / interpret the
numDF and denDF terms to others... or do they even matter to a person who is
looking to see if the 'treatment' predictor terms had any effect on the
response term?
Thanks in advance.
Dylan
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