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Table 2 Linear effect estimates for the LISA study: quantile boosting

From: Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

 

Cross-sectional analysis

Longitudinal analysis

Variable

τ= 0.025

τ= 0.975

τ= 0.025

τ= 0.975

Intercept

14.208

14.867

14.627

12.723

cAge

--

--

f(·)

f(·)

cBMI2

f(·)

f(·)

f(·)

f(·)

cBMI0

0.008

   

mBMI

0.028

0.034

0.029

0.132

mDiffBMI

0.026

   

cSex = male

 

0.068

  

cArea = urban

-0.029

-0.075

 

-0.043

cBreast = yes

    

mSmoke = yes

-0.228

0.296

 

0.158

mEdu = 1 (low)

 

0.162

 

0.162

mEdu = 2

 

0.406

 

0.176

mEdu = 3

 

0.130

 

-0.107

mEdu = 4 (high)

 

0.070

 

-0.092

  1. Resulting effect estimates for the borders of 95% PIs with the quantile boosting approach. Only effects of selected variables are displayed. Non-linear effect estimates are presented as Figure S1 and Figure S3 in Additional file 1.