class Regression_F2S extends Predictor
The Regression_F2S class performs functional linear regression with
scaler response and functional covariates.
y = a + <b(t),x(t)> + ε
where <b, x> denotes the inner product of b and x.
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Instance Constructors
- new Regression_F2S(x: MatriD, y: VectoD, t: VectoD, bf: DBasisFunction, technique: RegTechnique.RegTechnique = Cholesky, lambda: Double = 1E-4)
- x
the covariate matrix - treated as functional
- y
the response vector
- t
the time vector
- bf
the basis function object with derivatives support
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- val _1: VectorD
- def analyze(x_r: MatriD = x, y_r: VectoD = y, x_e: MatriD = x, y_e: VectoD = y): Regression_F2S
Analyze a dataset using this model using ordinary training with the 'train' method.
Analyze a dataset using this model using ordinary training with the 'train' method.
- x_r
the data/input matrix (training/full)
- y_r
the response/output vector (training/full)
- x_e
the data/input matrix (testing/full)
- y_e
the response/output vector (testing/full)
- Definition Classes
- Regression_F2S → Predictor
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def corrMatrix(xx: MatriD): MatriD
Return the correlation matrix for the columns in data matrix 'xx'.
Return the correlation matrix for the columns in data matrix 'xx'.
- xx
the data matrix shose correlation matrix is sought
- Definition Classes
- Predictor
- def crossValidate(k: Int): Double
Test the accuracy of the predicted results by cross-validation, returning the sse.
Test the accuracy of the predicted results by cross-validation, returning the sse.
- k
number of crosses and cross-validations (defaults to 10x).
- val e: VectorD
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def eval(yy: VectoD): Regression_F2S
Compute the error and useful diagnostics for the entire dataset.
Compute the error and useful diagnostics for the entire dataset.
- yy
the test response/output vector
- def eval(xx: MatriD = x, yy: VectoD = y): Regression_F2S
Compute the error and useful diagnostics for the entire dataset.
Compute the error and useful diagnostics for the entire dataset.
- xx
the test data/input matrix
- yy
the test response/output vector
- Definition Classes
- Regression_F2S → Model
- def fitMap: Map[String, String]
Return the quality of fit map.
- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, Regression_F2S)
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.
- cols
the columns of matrix x currently included in the existing model
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- Definition Classes
- Regression_F2S → Predictor
- See also
Fitfor index of QoF measures.
- final def getClass(): Class[_ <: AnyRef]
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- def getX: MatriD
Return the 'used' data matrix 'x'.
Return the 'used' data matrix 'x'. Mainly for derived classes where 'x' is expanded from the given columns in 'x_', e.g.,
QuadRegressionadd squared columns.- Definition Classes
- Regression_F2S → Predictor
- def getY: VectoD
Return the 'used' response vector 'y'.
Return the 'used' response vector 'y'. Mainly for derived classes where 'y' is transformed, e.g.,
TranRegression,Regression4TS.- Definition Classes
- Regression_F2S → Predictor
- def hashCode(): Int
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- @native() @HotSpotIntrinsicCandidate()
- def hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
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- Regression_F2S → Model
- final def isInstanceOf[T0]: Boolean
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- val modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
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- Model
- def modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
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- Model
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- def parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
- Definition Classes
- Regression_F2S → Model
- def predict(z: MatriD = x): VectoD
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
- z
the new matrix to predict
- Definition Classes
- Regression_F2S → Predictor
- def predict(z: VectoD): 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
- Definition Classes
- Regression_F2S → Predictor
- def predict(xf: FunctionS2S): Double
Predict the y-value at time point 'tt'.
Predict the y-value at time point 'tt'.
- xf
the given function of time
- def predict(z: VectoI): Double
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
- z
the vector to use for prediction
- Definition Classes
- Predictor
- def report: String
Return a basic report on the trained model.
Return a basic report on the trained model.
- Definition Classes
- Regression_F2S → Model
- See also
'summary' method for more details
- def residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Regression_F2S → Predictor
- final def synchronized[T0](arg0: => T0): T0
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- val t0: Double
- def test(itest: VectoI): Unit
Test the model on the testing set for cross-validation purposes
Test the model on the testing set for cross-validation purposes
- itest
indicies of the testing set
- def test(modelName: String, doPlot: Boolean = true): Unit
Test the model on the full dataset (i.e., train and evaluate on full dataset).
Test the model on the full dataset (i.e., train and evaluate on full dataset).
- modelName
the name of the model being tested
- doPlot
whether to plot the actual vs. predicted response
- Definition Classes
- Predictor
- val tn: Double
- def toString(): String
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- AnyRef → Any
- def train(itest: IndexedSeq[Int]): Regression_F2S
Train the model on the training set for cross-validation purposes
Train the model on the training set for cross-validation purposes
- itest
indicies of the testing set, to be excluded for training
- def train(xx: MatriD = x, yy: VectoD = y): Regression_F2S
Train the model using the smoothed data to find the regression coefficients 'b'.
Train the model using the smoothed data to find the regression coefficients 'b'.
- xx
the data/input matrix
- yy
the response.output vector
- Definition Classes
- Regression_F2S → Model
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- def x_i(i: Int): VectorD
The inner product of phi and a smoothed function
The inner product of phi and a smoothed function
- i
the i-th smoothed function function
- val xmoo: IndexedSeq[Smoothing_F]
- val z: MatrixD
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