Hello,
I wanted to post this question below, on the R-help forum, but I'm not sure
I succeeded because it said that I wasn't subscribed to the mailing list
yet.
Now I am subscribed, but will my question be accepted now automatically, or
should I submit it again?
Thanks in advance,
Violet Swakman
Hello everyone,
I'm having trouble understanding my output from a linear mixed effects
model (nlme :: lme), I hope someone can help me.
Say I'm interested in the effect of Tarsus length on the Bar length of
feathers.
I used an lme since some birds were living in the same territory, so
territory was included as random effect.
Both Bar length and Tarsus length are seen as numerical values, Territory
is seen as a factor.
#########################
m1 <- lme(Bar_length~Tarsus_length, random = ~ 1|Territory, data=data)
> summary(m1)
Linear mixed-effects model fit by REML
Data: min_s12
AIC BIC logLik
-104.1593 -99.98116 56.07963
Random effects:
Formula: ~1 | as.factor(Territory)
(Intercept) Residual
StdDev: 0.01023884 0.01072872
Fixed effects: Av_bar_length ~ Tarsus_av
Value Std.Error DF t-value p-value
(Intercept) 0.22391092 0.08472658 19 2.6427470 0.0160
Tarsus_av -0.00048219 0.00338510 2 -0.1424453 0.8998
Correlation:
(Intr)
Tarsus_av -0.999
Standardized Within-Group Residuals:
Min Q1 Med
Q3 Max
-2.02920116 -0.49093095 0.05736504 0.47632005 1.15944871
Number of Observations: 23
Number of Groups: 20
######################
I do not understand why the model needs 17 degrees of freedom to calculate
1 intercept and slope (just 1 numerical explanatory variable). Could anyone
maybe explain this to me?
When I use Season, a factor with 2 levels, as an explanatory variable the
same thing happens, the model takes 17 DF's to calculate the effect of
Season.
Thanks in advance,
Violet
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