class SimpleRegression extends Predictor with Error
The SimpleRegression
class supports simple linear regression. In this case,
the vector 'x' consists of the constant one and a single variable 'x_1', i.e.,
(1, x_1). Fit the parameter vector 'b' in the regression equation
y = b dot x + e = (b_0, b_1) dot (1, x_1) + e = b_0 + b_1 * x_1 + e
where 'e' represents the residuals (the part not explained by the model).
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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
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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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- SimpleRegression → 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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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 = b dot z, i.e.0, (b_0, b_1) dot (1, z_1).
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e.0, (b_0, b_1) dot (1, z_1).
- z
the new vector to predict
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- SimpleRegression → 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
train(): Unit
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b_0, b_1) dot (1, x_1) + e using the least squares method.
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b_0, b_1) dot (1, x_1) + e using the least squares method.
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- SimpleRegression → Predictor
- See also
http://www.analyzemath.com/statistics/linear_regression.html
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