hanaml.GeometricRegression {hana.ml.r} | R Documentation |
hanaml.GeometricRegression is a R wrapper for PAL Bi-variate GeometricRegression algorithm.
hanaml.GeometricRegression (conn.context, data = NULL, key = NULL, features = NULL, label = NULL, formula = NULL, decomposition = NULL, adjusted.r2 = NULL, pmml.export = NULL)
conn.context |
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data |
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key |
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features |
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label |
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formula |
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decomposition |
Defaults to "LU". |
adjusted.r2 |
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pmml.export |
Default to "no". |
R6Class
object.
Geometric regression is an approach used to model the relationship between a scalar variable y and a variable denoted X. In geometric regression, data is modeled using geometric functions, and unknown model parameters are estimated from the data. Such models are called geometric models.
Return a "GeometricRegression" object with following values:
coefficients: DataFrame
Fitted regression coefficients.
pmml: DataFrame
Regression model content in PMML format.
Set to None if no PMML model was requested.
model : DataFrame
Model is used to save coefficients or PMML model. If PMML model is requested,
model defaults to PMML model. Otherwise, it is coefficients.
fitted: DataFrame
Predicted dependent variable values for training data.
Set to None if the training data has no row IDs.
statistics: DataFrame
Regression-related statistics, like mean square error, F-statistics, etc.
## Not run: Training DataFrame df: > df ID Y X1 1 0 1.1 1 2 1 4.2 2 3 2 8.9 3 4 3 16.3 4 5 4 24.0 5 6 5 36.0 6 7 6 48.0 7 8 7 64.0 8 9 8 80.0 9 10 9 101.0 10 Train the model: > gr <- hanaml.GeometricRegression(conn.context = conn, data = df, key = 'ID', label = 'Y', pmml.export='multi-row') Output: > gr$coefficients VARIABLE_NAME COEFFICIENT_VALUE 1 __PAL_INTERCEPT__ 1.072219 2 X1 1.959633 ## End(Not run)