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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