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Table 5 Results for piecewise linear regression

From: Priming nonlinear searches for pathway identification

 

Interval 1

Interval 2

Interval 3

a10

0.1315

-0.0419

0.0000

a11

-42.3980

-14.1738

-14.5490

a12

0.0000

-0.8010

-0.0464

a13

8.9105

7.3653

7.6299

a14

12.7757

-0.3340

-0.1386

a15

-3.3476

-6.9121

-7.2940

a20

0.0567

-0.0197

0.0000

a21

-1.1939

14.4913

14.6792

a22

-32.3300

-14.5116

-14.6784

a23

0.6133

0.0057

-0.0205

a24

7.0917

0.1016

-0.0018

a25

7.9313

-0.1047

0.0067

a30

-0.7858

-0.0181

0.0000

a31

-130.3724

-0.2358

0.0021

a32

0.0000

0.3616

-0.0007

a33

-20.7724

-27.6129

-27.2551

a34

62.1525

0.3496

-0.0027

a35

19.1470

-0.1984

0.0006

a40

0.3164

-0.0709

0.0000

a41

-13.6819

1.1412

-0.0115

a42

0.0000

-2.1478

0.0015

a43

19.8295

18.8534

18.6927

a44

-13.3654

-19.5811

-18.5494

a45

-7.2135

-8.0985

-9.2792

a50

0.1617

-0.0393

0.0000

a51

-149.5199

-0.8195

0.0250

a52

-160.3341

0.8175

-0.0074

a53

5.7537

0.0580

-0.0304

a54

85.3050

19.0394

18.5356

a55

53.9745

-19.1183

-18.5623

  1. The complete dataset is divided into three subsets for each variable, where the first and second extreme values serve as breakpoints. The datasets for the regression consisted of 401 data points in the interval [0,4] and resulted from a simulation in which X3 was perturbed at t = 0 to a value 5% above its steady-state value.