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Table 3 Summary of fit statistics of mixed-effect models (3)–(8) and the respective prediction bias using only the fixed terms for validation. The best values per dataset are set in italics

From: Comparison of models for estimating bark thickness of Picea abies in southwest Germany: the role of tree, stand, and environmental factors

Dataset

Eq. (3)

Eq. (4)

Eq. (5)

Eq. (6)

Eq. (7)

Eq. (8)

LME

LME

LME

LME

LME

NLME

1 (1970s)

Fitting

MAE (mm)

1.87

1.76

1.01

1.58

1.85

1.14

RMSE (mm)

2.44

2.35

1.34

2.14

2.42

1.52

Validation (10-fold CV)

MAE (mm)

2.56

2.34

3.08

2.92

2.58

2.22

RMSE (mm)

3.17

3.07

4.04

3.78

3.29

2.94

2 (2014–16)

Fitting

MAE (mm)

1.91

1.95

1.20

1.44

1.98

1.26

RMSE (mm)

2.48

2.55

1.57

1.95

2.61

1.68

Validation (10-fold CV)

MAE (mm)

3.03

2.58

3.28

2.62

2.98

2.39

RMSE (mm)

3.75

3.31

4.19

3.42

3.69

3.14

  1. LME linear mixed-effects model, NLME nonlinear mixed-effects model, MAE mean absolute error, RMSE root mean square error