Hi Rick,
The method you mention could deal with the graph with the changing sign of slopes, eg. from positive to negative.
However, some graphs in my example are without the changing direction of slopes. I am wondering how to detect the changing point in this situation.
Below is my dataset. X= back_timescale, y = predicted.
Obs
back_timescale
Predicted
Lower
Upper
slope
SlopeChanged
1
-15
29.40
27.60
31.19
.
.
2
-14
28.86
27.49
30.22
-0.54
.
3
-13
28.44
27.37
29.52
-0.42
0.12
4
-12
28.13
27.25
29.02
-0.31
0.11
5
-11
27.91
27.13
28.69
-0.22
0.09
6
-10
27.76
27.06
28.47
-0.15
0.07
7
-9
27.67
27.02
28.31
-0.10
0.05
8
-8
27.60
27.01
28.20
-0.06
0.04
9
-7
27.56
27.01
28.11
-0.05
0.02
10
-6
27.51
27.00
28.03
-0.05
0.00
11
-5
27.45
26.95
27.95
-0.06
-0.02
12
-4
27.35
26.85
27.85
-0.10
-0.04
13
-3
27.20
26.70
27.70
-0.15
-0.05
14
-2
26.97
26.47
27.47
-0.22
-0.07
15
-1
26.66
26.15
27.16
-0.31
-0.09
16
0
26.24
25.69
26.78
-0.42
-0.11
This is the graph between x and y.
Since the back_timescale include limited point (-15 to 0 by 1 ), could I use different point as the spline point and then compare their fit statistics (AIC, BIC) to decide the best spline point?
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