scalation/scalation/scalation.modeling/Fit

Fit

scalation.modeling.Fit

See theFit companion object

trait Fit(var dfm: Double, var df: Double) extends FitM

The Fit trait provides methods to determine basic Quality of Fit QoF measures.

Value parameters

df: the degrees of freedom for error
dfm: the degrees of freedom for model/regression

Attributes

See also: reset to reset the degrees of freedom
Companion: object
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Supertypes: trait FitM

class Object

trait Matchable

class Any
Known subtypes: class ClusteringPredictor

class Diagnoser

class AR_Star

class RandomWalk_Star

class VAR

class Forecaster

class AR

class ARMA

class ARIMA

class Forecaster_D

class NeuralNet_3L4TS

class ARX_D

class ARX_Quad_D

class ARX_Symb_D

class ARY_D

class Forecaster_Reg

class ARX

class ARX_Quad

class ARX_Symb

class ARY

class ARY_Quad

class SARY

class NullModel

class RandomWalk

class RandomWalkS

class SimpleExpSmoothing

class SimpleMovingAverage

class TranARY

class TrendModel

class WeightedMovingAverage

class AR

class AR1MA

class ARIMA

class ARMA

class NullModel

class QuadSpline

class RandomWalk

class SimpleExpSmoothing

class SimpleMovingAverage

class TrendModel

class WeightedMovingAverage

class CNN_1D

class CNN_2D

class ELM_3L1

class NeuralNet_2L

class NeuralNet_3L

class NeuralNet_XL

class NeuralNet_XLT

class RegressionMV

class ExpRegression

class KNN_Regression

class LassoRegression

class NonlinearRegression

class NullModel

class Perceptron

class PoissonRegression

class Regression

class ARX

class ARX_Quad

class PolyORegression

class PolyRegression

class RegressionCat

class RegressionWLS

class RoundRegression

class TranRegression

class TrigRegression

class RegressionTree

class RegressionTreeGB

class RegressionTreeGB4TS

class RegressionTreeMT

class RegressionTreeMT4TS

class RegressionTreeRF

class RegressionTreeRF4TS

class RegressionTreeRF_MT4TS

class RegressionTreeRF_MT

class RidgeRegression

class SimpleExpRegression

class SimpleRegression

class SimplerRegression

class TestFit
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Members list

Value members

Concrete methods

Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

Value parameters

w: the weights on the instances (defaults to null)
y_: the actual response/output vector to use (test/full)
yp_: the predicted response/output vector (test/full)

Attributes

See also: Regression_WLS
Definition Classes: FitM

Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. Include interval measures. Note: wis should be computed separately.

Value parameters

alpha: the nominal level of uncertainty (alpha) (defaults to 0.9, 90%)
low: the predicted lower bound
up: the predicted upper bound
w: the weights on the instances (defaults to null)
y: the actual response/output vector to use (test/full)
yp: the point prediction mean/median

Attributes

See also: Regression_WLS

Diagnose the health of the model by computing the Quality of Fit (QoF) measures,

Value parameters

alphas: the array of prediction levels
low: the lower bounds for various alpha levels
up: the upper bounds for various alpha levels
y: the given time-series (must be aligned with the interval forecast)
yp: the point prediction mean/median

Attributes

Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.

Attributes

Definition Classes: FitM

Return the the y-transformation.

Attributes

Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.

Attributes

Definition Classes: FitM

The log-likelihood function times -2. Override as needed.

Value parameters

ms: raw Mean Squared Error
s2: MLE estimate of the population variance of the residuals

Attributes

See also: www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf

www.wiley.com/en-us/Introduction+to+Linear+Regression+Analysis%2C+5th+Edition-p-9780470542811 Section 2.11

Return the mean of the squares for error (sse / df). Must call diagnose first.

Attributes

Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.

Value parameters

df_update: the updated degrees of freedom (model, error)

Attributes

Show the prediction interval forecasts and relevant QoF metrics/measures.

Value parameters

h: the forecasting horizon
low: the predicted lower bound
qof_all: all the QoF metrics (for point and interval forecasts)
up: the predicted upper bound
yfh: the forecasts for horizon h
yy: the aligned actual response/output vector to use (test/full)

Attributes

Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.

Value parameters

b: the parameters/coefficients for the model
fname: the array of feature/variable names
vifs: the Variance Inflation Factors (VIFs)
x_: the testing/full data/input matrix

Attributes

Definition Classes: FitM

Inherited methods

Attributes

Inherited from:: FitM

Return the coefficient of determination (R^2). Must call diagnose first.

Attributes

Inherited from:: FitM

Return the symmetric Mean Absolute Percentage Error (sMAPE) score. Caveat: y_i = yp_i = 0 => no error => no percentage error

Value parameters

e_: the error/residual vector (if null, recompute)
y: the given time-series (must be aligned with the forecast)
yp: the forecasted time-series

Attributes

Inherited from:: FitM

Return the sum of the squares for error (sse). Must call diagnose first.

Attributes

Inherited from:: FitM

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