class NullModel extends Fit with Predictor with Error
The NullModel class implements the simplest type of predictive modeling technique
that just predicts the response 'y' to be the mean.
Fit the parameter vector 'b' in the regression equation
y = b dot x + e = b0 + e
where 'e' represents the residuals (the part not explained by the model).
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Instance Constructors
-
new
NullModel(y: VectoD)
- y
the response vector
Value Members
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
- Definition Classes
- Predictor
-
val
df: (Double, Double)
- Definition Classes
- Fit
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def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null): Unit
Given the error/residual vector, compute the quality of fit measures.
Given the error/residual vector, compute the quality of fit measures.
- e
the corresponding m-dimensional error vector (y - yp)
- w
the weights on the instances
- yp
the predicted response vector (x * b)
- Definition Classes
- Fit
-
def
eval(): Unit
Compute the error and useful diagnostics.
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def
eval(xx: MatriD, yy: VectoD): Unit
Compute the error and useful diagnostics for the test dataset.
Compute the error and useful diagnostics for the test dataset.
- xx
the test data matrix
- yy
the test response vector FIX - implement in classes
- Definition Classes
- Predictor
-
def
f_(z: Double): String
Format a double value.
-
def
fit: VectoD
Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.
Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. 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). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures. Override to add more quality of fit measures.
- Definition Classes
- Fit
-
def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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val
index_rSq: Int
- Definition Classes
- Fit
-
def
mse_: Double
Return the mean of squares for error (sse / df._2).
Return the mean of squares for error (sse / df._2). Must call diagnose first.
- Definition Classes
- Fit
-
def
predict(z: VectoD): Double
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', i.e., '[b0] dot [z0]'.
-
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
- Definition Classes
- Predictor
-
def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
-
def
sumCoeff(b: VectoD, stdErr: VectoD = null): String
Produce the summary report portion for the cofficients.
Produce the summary report portion for the cofficients.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
-
def
summary(b: VectoD, stdErr: VectoD = null): String
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
-
def
train(yy: VectoD = y): NullModel
Train the predictor by fitting the parameter vector (b-vector) in the simpler regression equation.