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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new
SimpleRegression(x: MatrixD, y: VectorD)
- x
the input/design matrix augmented with a first column of ones
- y
the response vector
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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def
clone(): AnyRef
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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: VectoD): Unit
Compute diagostics for the regression model.
Compute diagostics for the regression model.
- yy
the response vector
- Definition Classes
- SimpleRegression → Predictor
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val
e: VectoD
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final
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eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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def
fit: VectorD
Return the quality of fit including.
Return the quality of fit including.
- Definition Classes
- SimpleRegression → Predictor
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def
fitLabels: Seq[String]
Return the labels for the fit.
Return the labels for the fit.
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- SimpleRegression → Predictor
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final
def
flaw(method: String, message: String): Unit
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val
mae: Double
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def
notify(): Unit
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def
notifyAll(): Unit
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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
- Definition Classes
- SimpleRegression → Predictor
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def
predict(z: VectoI): 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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val
rSq: Double
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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val
rmse: Double
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val
sse: Double
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val
ssr: Double
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val
sst: Double
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synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(): Unit
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation for the response passed into the class 'y'.
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation for the response passed into the class 'y'.
- Definition Classes
- SimpleRegression → Predictor
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def
train(yy: VectoD): Unit
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation
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.
- yy
the response vector
- Definition Classes
- SimpleRegression → Predictor
- See also
www.analyzemath.com/statistics/linear_regression.html
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