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- LinearRegr(data=None, data_filter_expr=None, variables_count=2, weights=False, formula=None, algorithm=None, coeff_stats=False, conf_int_level=0.9, model_stats=False, residuals=False, **generic_arguments)
- DESCRIPTION:
The LinearRegr() function is a simple linear regression function.
It fits data to a curve using a formula that defines
the relationship between the explanatory variable and the
response variable.
The following procedure is an example of how to use
LinearRegr() to develop an ARIMA model:
* Determine that the series to be modeled includes a trend.
* Use LinearRegr() to remove the trend from the series.
* Use the "fitmetadata" attribute from the function output,
to determine the trend by fitting the data set.
* Use GenseriesFormula() to generate a trend series.
* Use BinarySeriesOp() to subtract the generated trend
from the original series.
PARAMETERS:
data:
Required Argument.
Specifies an input time series with the following payload characteristics:
* "payload_content" value is MULTIVAR_REAL.
* "payload_fields" has two required fields (response variable and
explanatory variable, in that order) and one optional
field (weights).
Types: TDSeries
data_filter_expr:
Optional Argument.
Specifies filter expression for "data".
Types: ColumnExpression
variables_count:
Optional Argument.
Specifies the number of parameters present
in the payload. For linear regression with no weighting,
there are 2 parameters (the response variable and the explanatory
variable). For linear regression with weighting, there are
3 variables (the response variable, the explanatory variable,
and the weights).
Default Value: 2
Permitted Values: 2, 3
Types: int
weights:
Optional Argument.
Specifies whether a third series is present
in MULTIVAR series-specifications. The third series is
interpreted as a series of weights that can be used to
perform a weighted least-squares regression solution.
When set to False, no third series is present,
otherwise it is present.
Default Value: False
Types: bool
formula:
Required Argument.
Specifies the formula that is to be used in the regression operation.
The formula defines the relationship between the explanatory
variable and the response variable and, conforms to Formula Rules.
Note:
Use the following link to refer the formula rules in Teradata document(func_param):
"https://docs.teradata.com/r/4k28qKyhFXQ3DA~TULEIuw/Yp9oQ2nOzr70tKke4rCiAQ"
Types: str
algorithm:
Required Argument.
Specifies the algorithm used for the regression.
Permitted Values:
1. QR: means that QR decomposition is used for the regression.
2. PSI: means that pseudo-inverse based on singular value
decomposition (SVD) is used to solve the regression.
Types: str
coeff_stats:
Optional Argument.
Specifies whether to include coefficient statistics columns in the results.
When set to False, coefficient statistics columns are not included in
the results, otherwise, columns are included in the results.
Default Value: False
Types: bool
conf_int_level:
Optional Argument.
Specifies the confidence interval level value used for coefficient
statistics calculation. The value is greater than 0 and less than 1.
Note:
Applicable only when "coeff_stats" is set to True.
Default Value: 0.9
Types: float
model_stats:
Optional Argument.
Specifies whether to generate the optional model statistics and
available to access using the attribute "fitmetadata" of the function
output. When set to True, function generates the model statistics,
otherwise not.
Default Value: False
Types: bool
residuals:
Optional Argument.
Specifies whether to generate the tertiary (residuals)
layer and available to access using the attribute "fitresiduals" of
the function output. When set to True, function generates the layer,
Otherwise not.
Default Value: False
Types: bool
**generic_arguments:
Specifies the generic keyword arguments of UAF functions.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Note that, when UAF function is executed, an
analytic result table (ART) is created.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile ART or not. When set to
True, results are stored in a volatile ART,
otherwise not.
Default Value: False
Types: bool
output_table_name:
Optional Argument.
Specifies the name of the table to store results.
If not specified, a unique table name is internally
generated.
Types: str
output_db_name:
Optional Argument.
Specifies the name of the database to create output
table into. If not specified, table is created into
database specified by the user at the time of context
creation or configuration parameter. Argument is ignored,
if "output_table_name" is not specified.
Types: str
RETURNS:
Instance of LinearRegr.
Output teradataml DataFrames can be accessed using attribute
references, such as LinearRegr_obj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. result
2. fitmetadata - Available when "model_stats" is set to True, otherwise not.
3. fitresiduals - Available when "residuals" is set to True, otherwise not.
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
# Load the example data.
load_example_data("uaf", ["house_values2"])
# Create teradataml DataFrame object.
data = DataFrame.from_table("house_values2")
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=data,
id="cid",
row_index_style="SEQUENCE",
row_index="s_no",
payload_field=["house_value", "salary"],
payload_content="MULTIVAR_REAL")
# Example 1: The LinearRegr() function fits TDSeries data to
# the curve mentioned in the "formula." It returns
# a result containing solved coefficients, model statistics,
# and residuals statistics.
uaf_out = LinearRegr(data=data_series_df,
variables_count=2,
weights=False,
formula="Y=B0+B1*X1",
algorithm='QR',
model_stats=True,
coeff_stats=False,
residuals=True)
# Print the result DataFrames.
print(uaf_out.result)
# Print the model statistics result.
print(uaf_out.fitmetadata)
# Print the residuals statistics result.
print(uaf_out.fitresiduals)
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