R: Bi-variate Natural Logarithmic Regression

hanaml.LogarithmicRegression {hana.ml.r}

R Documentation

Bi-variate Natural Logarithmic Regression

Description

hanaml.LogarithmicRegression is a R wrapper for PAL Bi-variate natural logarithmic regression algorithm.

Usage

hanaml.LogarithmicRegression (conn.context,
                             data = NULL,
                             key = NULL,
                             features = NULL,
                             label = NULL,
                             formula = 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.
`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

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

Value

Return a "LogarithmicRegression" 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.

Examples

## Not run: 
Training DataFrame df:
 > df$Collect()
   ID   Y X1
 1  0  10  1
 2  1  80  2
 3  2 130  3
 4  3 160  4
 5  4 180  5
 6  5 190  6
 7  6 192  7

Training:
 > nlr <-  hanaml.LogarithmicRegression(conn.context = conn,
                                        data = df,
                                        key = 'ID',
                                        label = 'Y',
                                        pmml.export='multi-row')

Output:

 > nlr$coefficients

       VARIABLE_NAME COEFFICIENT_VALUE
 1 __PAL_INTERCEPT__           14.8616
 2                X1           98.2936


## End(Not run)

[Package hana.ml.r version 1.0.8 Index]