class NonLinRegression extends Predictor with Error
The NonLinRegression
class supports non-linear regression. In this case,
'x' can be multi-dimensional '[1, x1, ... xk]' and the function 'f' is non-linear
in the parameters 'b'. Fit the parameter vector 'b' in the regression equation
y = f(x, b) + e
where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector 'b' by using Non-linear Programming to minimize Sum of Squares Error 'SSE'.
- See also
www.bsos.umd.edu/socy/alan/stats/socy602_handouts/kut86916_ch13.pdf
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val
b: VectoD
Coefficient/parameter vector [b_0, b_1, ...
Coefficient/parameter vector [b_0, b_1, ... b_k]
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
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def
diagnose(yy: VectorD, e: VectorD): Unit
Compute diagostics for the regression model.
Compute diagostics for the regression model.
- yy
the response vector
- e
the residual/error vector (not used directly)
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val
e: VectoD
Residual/error vector [e_0, e_1, ...
Residual/error vector [e_0, e_1, ... e_m-1]
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def
fit: VectorD
Return the quality of fit including 'rSquared'.
Return the quality of fit including 'rSquared'.
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- NonLinRegression → Predictor
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def
fitLabels: Array[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
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final
def
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
- method
the method where the error occurred
- message
the error message
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def
predict(z: VectoD): Double
Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.e.0, (b0, b1) dot (1.0, z1).
Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.e.0, (b0, b1) dot (1.0, z1).
- z
the new vector to predict
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- NonLinRegression → Predictor
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def
predict(z: VectorI): Double
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
- z
the vector to use for prediction
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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def
sseF(b: VectorD): Double
Function to compute the Sum of Squares Error 'SSE' for given values for the parameter vector 'b'.
Function to compute the Sum of Squares Error 'SSE' for given values for the parameter vector 'b'.
- b
the parameter vector - FIX - to
VectoD
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def
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def
train(): Unit
Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation y = f(x, b) using the least squares method.
Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation y = f(x, b) using the least squares method. Caveat: Optimizer may converge to an unsatisfactory local optima. If the regression can be linearized, use linear regression for starting solution.
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