hanaml.LogarithmicRegression.Rd
hanaml.LogarithmicRegression is a R wrapper for SAP HANA PAL Bi-variate natural logarithmic regression algorithm.
hanaml.LogarithmicRegression(
data = NULL,
key = NULL,
features = NULL,
label = NULL,
formula = NULL,
decomposition = NULL,
adjusted.r2 = NULL,
pmml.export = NULL
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
If not provided, the data is assumed to have no ID column.
No default value.
character, optional
Name of the feature column.
If not provided, it defaults the first non-key, non-label column of data.
character, optional
Name of the column which specifies the dependent variable.
Defaults to the last column of data if not provided.
formula type, optional
Formula to be used for model generation.
format = label~<feature_list>
e.g.: formula=CATEGORY~V1+V2+V3
You can either give the formula,
or a feature and label combination, but do not provide both.
Defaults to NULL.
c("LU", "QR", "SVD", "Cholesky"), optional
Specifies decomposition method(case-insensitive).
"LU":
Doolittle decomposition.
"QR":
QR decomposition.
"SVD":
singular value decomposition.
"Cholesky":
Cholesky decomposition.
Defaults to "QR".
logical, optional
If TRUE, include the adjusted R^2 value in the statistics table.
Defaults to FALSE.
c("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".
Returns a "LogarithmicRegression" object with following values:
coefficients: DataFrame
Fitted regression coefficients.
pmml: DataFrame
Regression model content in PMML format.
Set to NULL 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 NULL if the training data has no row IDs.
statistics: DataFrame
Regression-related statistics, like mean square error, F-statistics, etc.
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.
Input DataFrame data:
> data$Collect()
ID Y X1
1 0 10 1
2 1 80 2
3 2 130 3
4 3 160 4
5 4 180 5
6 5 190 6
7 6 192 7
Call the function:
> nlr <- hanaml.LogarithmicRegression(data = df,
key = "ID",
label = "Y",
pmml.export="multi-row")
Output:
> nlr$coefficients$Collect()
VARIABLE_NAME COEFFICIENT_VALUE
1 __PAL_INTERCEPT__ 14.8616
2 X1 98.2936