Why do not have an intercept in lmer model (fixed effects) and how to interpret the result?

Machine Learning and Modeling
1

I do not understand why I do not have an intercept in output of my mixed effect model, I do not know how to interpret the result (fixed and random effects). variable preste present the loss in rolling surface, pente mean slope, classeFunc is the functional class of the road and idp_troncon_tembec mean section identifier.

Blockquote
Fixed effects:
Estimate Std. Error t value
pente -0.9215 0.2199 -4.191
agelog:classeFunc1 -6.2900 1.2868 -4.888
agelog:classeFunc2 -8.9555 1.1762 -7.614
agelog:classeFunc3 -11.7433 1.0861 -10.812

Random effects:
Groups Name Variance Std.Dev.
idp_troncon_tembec agelog 39.00 6.245
Residual 81.68 9.038
Number of obs: 192, groups: idp_troncon_tembec, 111

Correlation of Fixed Effects:
pente agl:F1 agl:F2
aglg:clssF1 -0.219
aglg:clssF2 -0.264 0.058
aglg:clssF3 -0.297 0.065 0.078

2

Hi,

Please provide us with a minimal reproducible example and some more details on the model you're trying to use.

Good luck

3

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