R: Polynomial Regression

hanaml.PolynomialRegression {hana.ml.r}

R Documentation

Polynomial Regression

Description

hanaml.PolynomialRegression is a R wrapper for PAL Polynomial Regression.

Usage

hanaml.PolynomialRegression (conn.context,
                             data = NULL,
                             key = NULL,
                             features = NULL,
                             label = NULL,
                             formula = NULL,
                             degree = NULL,
                             decomposition = NULL,
                             adjusted.r2 = NULL,
                             pmml.export = NULL)

Arguments

`conn.context`	`ConnectionContext` The connection to the SAP HANA system.
`data`	`DataFrame` DataFrame containing the data.
`key`	`character, optional` Name of the ID column. If not provided, the data is assumed to have no ID column. No default value.
`features`	`character, optional` Name of the feature column. If not provided, it defaults the the first non-ID, non-label column of data.
`label`	`character` Name of the column in data that specifies the dependent variable. Defaults to the last column of data if not provided.
`formula`	`formula type, optional` Formula to be used for model generation. format = label~<feature_list> e.g.formula = LABEL~V1+V2+V3 You can either give the formula, or a features and label combination. Do not provide both. Defaults to NULL.
`degree`	`integer` Degree of the polynomial model.
`decomposition`	`{"LU", "SVD"},optional` Specifies decomposition method. `"LU":` Doolittle decomposition. `"SVD":` singular value decomposition. Defaults to "LU".
`adjusted.r2`	`logical, optional` If TRUE, include the adjusted R^2 value in the statistics table. Defaults to FALSE.
`pmml.export`	`{"no", "single-row", "multi-row"}, optional` Controls whether to output a PMML representation of the model, and how to format the PMML. `"no":` No PMML model. `"single-row":` Exports a PMML model in a maximum of one row. Fails if the model doesn't fit in one row. `"multi-row":` Exports a PMML model, splitting it across multiple rows if it doesn't fit in one. Default to "no".

Format

R6Class object.

Details

Polynomial regression is an approach to modeling the relationship between a scalar variable y and a variable denoted X. In polynomial regression, data is modeled using polynomial functions, and unknown model parameters are estimated from the data. Such models are called polynomial models.

Value

coefficients : DataFrame
Fitted regression coefficients.
pmml : DataFrame
PMML model. Set to NULL if no PMML model is 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 NULL if the training data has no row IDs.
statistics : DataFrame
Regression-related statistics, such as mean squared error.

Examples

## Not run: 
Input DataFrame df for training:
>df$Collect()
       ID   Y X1
     1  0   5  1
     2  1  20  2
     3  2  43  3
     4  3  89  4
     5  4 166  5
     6  5 247  6
     7  6 403  7

Training the model:
>pr <- hanaml.PolynomialRegression(conn, data.fit, key = "ID", formula= Y~X1,
                                   degree = 3L, pmml.export = "multi-row")

Output:
> pr$coefficients$Collect()
      VARIABLE_NAME COEFFICIENT_VALUE
1  __PAL_INTERCEPT__        -11.000000
2 X1__PAL_DELIMIT__1         17.250000
3 X1__PAL_DELIMIT__2         -3.416667
4 X1__PAL_DELIMIT__3          1.333333

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]