Transcript Non-linear Regression Analysis

Non-linear Regression Analysis
with Fitter Software Application
Alexey Pomerantsev
Semenov Institute of Chemical Physics
Russian Chemometrics Society
12.02.02
1
Agenda
1. Introduction
2. TGA Example
3. NLR Basics
4. Multicollinearity
5. Prediction
6. Testing
7. Bayesian Estimation
8. Conclusions
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1. Introduction
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Linear and Non-linear Regressions
Linear
f  a11 ( x)   a p p ( x)
any
f (pa, xp)( x)
f f=aa1exp(-20x)
1 ( x)  
f=exp(-ax)
a
Formula
Example
Source
Choice
Dimension
Soft
Interpretation
Soft Tools
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Hard
Close relatives?
Multicollinearity
Purpose
Non Linear
any f (a, x)
Easy ?
Large
Difficult ?
Small
Excess of parameters
Well-known
Interpolation
Many
Lack of data
Uncommon
Extrapolation
Few
4
2. Thermo Gravimetric Analysis Example
Object
Goal
PVC Cable Isolation
Let’s see it!
Service-Life Prediction
Experiment
Thermo Gravimetric Method
Tool
Non-Linear Regression and Fitter
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TGA Experiment and Data
TGA Experiment
TGA Data
510
Sample
490
0.98
470
0.96
450
430
0.94
410
0.92
390
0.90
HEAT
Temperature T , K
Plasticizer
Chamge in mass, y
1.00
370
0
10
20
30
40
50
Time t , min
This is Experiment! Not a Hell of Flame!
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TGA Example Variables
Estimated
Measured
Intermediate
Response
y=m/m
Change in mass
0
Predictors
t
Time
C
Plasticizer concentration
Small size
problem!
Parameters
y0
Initial value of y
C0
Initial concentration
k0
Evaporation rate constant
v
T0
Heating rate
E
Activation energy
F
Sample specific surface
Initial temperature
Sample specific surface F  S  22 R 2
V R -r
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Plasticizer Evaporation Model
Evaporation Law
dy  -k C, y(0)  y
0
dt
Diffusion is
not relevant!
Volume Change
The Arrhenius law
Temperature growth
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C  1-
1- C0
y
æ
ç
ç
ç
è
ö
E
÷
k  F exp k0 ÷
RT ÷ø
T=T0+vt
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Fitter Worksheet for TGA Example
A
B
C
D
E
F
C
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
F
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
t
0.00
1.15
2.25
3.40
4.55
5.65
6.75
7.85
9.00
10.10
0.4
0.4
0.4
0.4
0.4
2
2
2
2
2
3E+06
5E+06
8E+06
1E+07
1E+07
G
H
I
J
K
L
M
N
y
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
f
1.001
1.001
1.001
1.001
1.001
1.001
1.001
1
1
1
Left
1.001
1.001
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
'TGA Desorbtion Model
D[y]/D[t]=-k*[1-(1-C0)/y];y(0)=y0
k=F*exp(k0-E/R/T)
T=T0+v*t
R=1.98717
y0=?
k0=?
E=?
0.931
0.871
0.821
0.781
0.748
0.915
0.844
0.788
0.746
0.714
Parameters estimation
Name Initial
Final
Deviation
y0
1 1.00088
0.0002
k0
10 13.9964 0.24016
10000 18052.3 225.424
E
O
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Data
v T0
3 373
3 373
3 373
3 373
3 373
3 373
3 373
3 373
3 373
3 373
0
0
0
0
0
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293
293
293
293
293
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Service Life Prediction by TGA Data
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3. NLR Basics
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Data and Errors
Response
y1
y2
.
.
.
.
y
yN
Predictors
x 11 x 12
x 21 x 22
.
.
.
.
.
.
.
.
.
.
.
Weights
x 1m
x 2m
Weight
is
.
.
.
.
a
w
X
.
.
an effective
.
.
instrument!
.
.
.
.
x N1 x N2
a1
a2
ap
x Nm
Absolute error
yi  f i  e i
Relative error
yi  f i (1  e i )
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w1
w2
Parameters
wN
Fit
f1
f2
.
.
f
.
fN
Weight and variance
wi2  cov(e i , e i )  s e2  Const
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Model f(x,a)
'Цикл "увлажнение-сушка"
M=Sor*hev(t1-t)+Des*[hev(t-t1)+imp(t-t1)]
'Кинетика "увлажнения"
Sor=Sor1*hev(USESor1)+Sor2*[hev(-USESor1)+imp(-USESor1)]
'Кинетика "сушки"
Des=Des1*hev(USEDes1)+Des2*[hev(-USEDes1)+imp(-USEDes1)]
'Условие применимости асимптотик
USESor1=Sor2-Sor1
USEDes1=Des1-Des2
'константы и промежуточные величины
t3=(t-t1)*hev(t-t1)
t4=t*hev(t1-t)+t1*[hev(t-t1)+imp(t-t1)]
P2=PI*PI
P12=(PI)^(-0.5)
R=r*(M1-M0)*exp(-r*t4)
K=M1+(M0-M1)*exp(-r*t4)
V0=M0-C0
V1=M1-C0
'асимптотика сорбции при 0<t<tau
Sor1=C0+4*P12*(d*t)^0.5*[M0-C0+(M1-M0)*beta]
beta=1-exp(-z)
x=r*t
z=(a1*x+a2*x*x+a3*x*x*x)/(1+b1*x+b2*x*x+b3*x*x*x)
a1=0.6666539250029
a2=0.0121051017749
a3=0.0099225322428
b1=0.0848006232519
b2=0.0246634591223
b3=0.0017549947958
'кинетика сорбции при tau<t<t1
Sor2=K-8*S1
S1=U01/n0+U11/n1+U21/n2+U31/n3+U41/n4
n0=P2
U01=[(V0*n0*d-V1*r)*exp(-n0*d*t4)+R]/(n0*d-r)
n1=P2*9
U11=[(V0*n1*d-V1*r)*exp(-n1*d*t4)+R]/(n1*d-r)
n2=P2*25
U21=[(V0*n2*d-V1*r)*exp(-n2*d*t4)+R]/(n2*d-r)
n3=P2*49
U31=[(V0*n3*d-V1*r)*exp(-n3*d*t4)+R]/(n3*d-r)
n4=P2*81
U41=[(V0*n4*d-V1*r)*exp(-n4*d*t4)+R]/(n4*d-r)
'асимптотика десорбции при t1<t<t1+tau
Des1=K*[1-4*P12*(d*t3)^0.5]-8*S1
'кинетика десорбции при t1+tau<t
Des2=8*S2
S2=U02/n0+U12/n1+U22/n2+U32/n3+U42/n4
U02=(K-U01)*exp(-n0*d*t3)
U12=(K-U11)*exp(-n1*d*t3)
U22=(K-U21)*exp(-n2*d*t3)
U32=(K-U31)*exp(-n3*d*t3)
U42=(K-U41)*exp(-n4*d*t3)
'неизвестные параметры
d=?
M0=?
M1=?
C0=?
r=?
t1=?
Different shapes of the same model
Explicit model
Implicit model
Diff. equation
y = a + (b – a)*exp(–c*x)
Rather
d[y]/d[x] = – c (y –a); y(0) = b
complex
model!
0 = a + (b – a)*exp(–c*x) – y
*
Presentation at worksheet
' Explicit model
y=a+(b-a)*exp(-c*t)
a=?
b=?
c=?
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'
Diff. equation
d[y]/d[t]=-c*(y-a); y(0)=b
a=?
b=?
c=?
13
Data & Model Prepared for Fitter
Predictor
A
B
1
2
3
4
5
6
7
8
9
10
11
C
D
Values
Response
Comment
Weight
E
F
G
H
I
J
K
L
M
Apply Fitter!
BoxBod Data
x
y w
f
0
0
0.00
1 109 1 90.11
2 149 1 142.24
3 149 1 172.41
5 191 1 199.95
7 213 1 209.17
10 224 1 212.91
Parameters
a
100
213.80941
b
0.4
0.5472375
'BoxBOD model
y=a*[1-exp(-b*x)]
a=?
b=?
y
200
100
0
0
Fitting
4
8
x
Equation
Parameters
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Objective Function Q(a)
N
Sum of squares
S (a)  å wi2 ( yi - f i ) 2 g i2
i 1
[
]Q
Objective function
is a sum
Q(a )  Sof
(a )  Bsquares
(a )
and may be more…
Bayesian term
B(a )  s02 N 0  (a - b) t H (a - b)
Objective function
Parameter estimates
aˆ  arg min Q(a)
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Weighted variance estimate
S (aˆ )
s 
Nf
2
Nf  N- p
15
Very Important Matrix A
Hesse’s
matrix
Gauss’
approximation
Aab
1 ¶ 2 Q (a )

, a , b  1,K, p
2 ¶aa ¶a b
A  V tV ( X t X in linear regression)
Matrix A is the
¶ f ( x , a)
V w
, a  1,..., p; i  1,.., N
cause of¶ a troubles..
Model
derivatives
ai
i
i
a
Covariance
matrix
C  s 2 A-1  F -1
F-matrix
F  s -2 A  C -1
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Quality of Estimation
Covariances Matrix
Deviations Vector
cov (aˆ, aˆ )  C  s 2 A-1
dev (aˆa )  Caa
Matrix Acor (is
the
C
aˆ , aˆ ) 
C C
measure of quality!
a
Correlations Matrix
Final Objective Value
Error Variance and Number
Degrees of Freedom
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ab
b
aa
bb
Q(aˆ)  min Q(a)
S (aˆ )
s 
Nf
2
Nf  N- p
17
Search by Gradient Method
Q (a ) 
Q(a n )  bnt (a - a n ) 
1
(a - a n ) t A (a - a n )
2
a n1  a n  A  bn
Matrix A is the
key
to
search!
a
Search problems
Initial point
0
det( A)  0
Local minima
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Objective function Q (a )
A A  I
Parameters a
a 7aa6 a5ˆ 4 a 3
a2
a1
a0
18
4. Multicollinearity
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Multicollinearity: View
Multicollinearity is degradation of matrix A
Objective function Q(a)
Spread of eigenvalues
æ l max
N( A)  log 10 ç
ç l min
è
ö
÷
÷
ø
8
7.2
6.4
a1
5.6
5.6
4.8
4.8
44
3.2
3.2
2.4
1.6
0.8
00
1.2
1.2
a2
2.4
3.6
4.8
4.8
6
7.2
7.2
is a measure of
degradation
N(A) = 2
1
7
6
5
4
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Multicollinearity: Source
“Hard” multicollinearity
“Soft” multicollinearity
(
)
y  a1 1 - e -a2 x  a1a2 x at a2 x << 1
y  a1a2 x
y
aa22xx=0.05
=1.0
=0.5
=0.1
0.05
0.1
0.8
0.4
00
00
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10
10
x
21
Data & Model Preprocessing
((a + b) + c) + d  a + (b + (c + d)) as
1+10 –20 = 1
Representation of a number in computer with 64 bits
1
0
0
1
…
0
1
1
Target is to make Hesse matrix A regular and decrease N(A)
M
e
a
n
s
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Data Scaling
Data Centering
Model Adjusting
X  mX
X  X – X0
a   (a) x   (x)
y   (y)
22
Example: The Arrhenius Law
Standard form
Adjusted form
æ E ö
k exp ç ÷
è RT ø
exp (a1 - a2 X )
Scaling & centering
1000
X 
- X0
T
1 n 1000
X0  å
n i 1 Ti
Adjusting
a1  ln (k ) - a2 X 0
a2 
k  10+11
E  10+4
N(A) = 20
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a1  1
E
1000 R
a2  10
N(A) = 2
23
Derivative Calculation and Precision
N(A)
A-1
y=f (a,x)
0=f (y,a,x)
dy/dx=f (y,a,x)
6
10
8
6+2=8
10+2=12
8+2=10
8+0=8
12+0=12
10+0=10
8+2=10
10+2=12
12+2=14
14+2=16
10+2=12
12+2=14
1) Numerical calculation of difference derivatives
C
2) Auto calculation of analytical derivatives
f=exp(-a*t)
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
df/da=-t*exp(-a*t)
24
5. Prediction
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Reliable Prediction
Estimate of response
Confidence limits
Linearization
yˆ  f ( x, aˆ )
Prob{l ( x, P) < f ( x, a) < r ( x, P) } ³ P
r ( x , P)  f ( x , aˆ )  g ( P) v t Cv
Forecast should
include uncertainties!
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Nonlinearity and Simulation
*
a * ~ N (aˆ, C )  a1* ,K, a*M  f 1* ,K, f M
 r ( P,x)
Non-linear models call
for special methods of
reliable prediction!
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Prediction: Example
Model of
rubber aging
Y  1 - e -( kt) ,
a
Accelerated aging tests
T=383K
Y
1.00
T=368K
1.00
Simulation
Mean
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
0.00
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T=293K
Linearization
T=353K
24
E
RT
Upper confidence limits
Y
0
k e
k0 -
72
time, hr
96
120
0
60
120 180 240
time, day
300
360
28
6. Testing
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Hypotheses Testing
Test statistics x is compared with critical value t (a)
From
experiment
Test don’t prove a model!
It just shows that
the hypothesis is
accepted or rejected!
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From
theory
30
Lack-of-Fit and Variances Tests
These hypotheses are based on variances
and they can’t be tested without replicas!
100
Replica 1
6
Variances
by
replicas
Replica 2
50
Lack-of-Fit
is a wily test!
4
Variance
Response
75
2
25
0
0
0.5
1.5
2.5
3.5
4.5
5.5
Predictor
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Outlier and Series Tests
These hypotheses are based on residuals
and they can be tested without replicas
Response
Positive
residual
Acceptable
deviation
1.2
Series test is
very sensitive!
5
Series of signs
Positive residuals 6
Negative residuals 11
1
0.8
Negative
residual
0.6
Outlier
0.4
0.2
0
5
10
15
Predictor
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7. Bayesian Estimation
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Bayesian Estimation
How to eat away
an elephant?
Slice by slice!
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Posterior and Prior Information. Type I
Posterior Information
Prior Information
Parameter estimates aˆ
Prior parameter values b
Matrix F
Recalculated matrix H
Error variance s2
Prior variance value
NDF
Prior NDF
The same error sin
N
each
portion ofN data!
f
0
2
0
Objective Function
Q ( a )  S ( a )  B (a )
B(a )  s02 (N 0  R(a ) ) R(a )  (a - b) t H (a - b)
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Posterior and Prior Information. Type II
Posterior Information
Prior Information
Parameter estimates aˆ
Prior parameter values b
Matrix F
Recalculated matrix H
Different errors in
Q (a )  S (a ) B (a )
each
portion
of
data!
æ R (a ) ö
B(a )  exp ç
R (a )  (a - b ) H (a - b )
÷
Objective Function
t
è N ø
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8. Conclusions
Mysterious Nature
LR Model
NLR Model
Thank
you!
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