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- BreuschPaganGodfrey(data=None, data_filter_expr=None, variables_count=None, formula=None, studentize=False, significance_level=0.05, **generic_arguments)
- DESCRIPTION:
The BreuschPaganGodfrey() function checks for heteroscedasticity using
one or more variables among the residual terms after running a regression.
The following procedure is an example of how to use BreuschPaganGodfrey() function:
* Use MultivarRegr() to create regression model and generate residuals.
* Use BreuschPaganGodfrey() on the output DataFrame from MultivarRegr().
* Check the 'NULL_HYPOTHESIS' value to determine if there is heteroscedasticity.
ACCEPT means that variance is homoscedastic.
REJECT means that variance is heteroscedastic.
PARAMETERS:
data:
Required Argument.
Specifies the residual multivariate series or
TDAnalyticResult object created over output
generated by the UAF regression function.
Types: TDSeries, TDAnalyticResult
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
variables_count:
Required Argument.
Specifies the number of explanatory variables
that are used in the auxiliary regression.
Types: int
formula:
Optional Argument.
Specifies the regression formula to use for the
auxiliary regression. If a formula is not included,
then the default regression formula is used.
Types: str
studentize:
Optional Argument.
Specifies whether to use the Koenker studentized
version of the Breusch-Pagan-Godfrey (BPG) test.
When set to True, Koenker studentized version is
used, otherwise the standard BPG test is used.
Default Value: False
Types: bool
significance_level:
Optional Argument.
Specifies the desired significance level for the test.
Default Value: 0.05
Types: float
**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 BreuschPaganGodfrey.
Output teradataml DataFrames can be accessed using attribute
references, such as BreuschPaganGodfrey_obj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. result
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_values"])
# Create teradataml DataFrame object.
data = DataFrame.from_table("house_values")
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=data,
id="cityid",
row_index="TD_TIMECODE",
payload_field=["house_val","salary","mortgage"],
payload_content="MULTIVAR_REAL")
# Create Multivariate regression model.
mvr_out = MultivarRegr(data=data_series_df,
variables_count=3,
weights=False,
formula="Y = B0 + B1*X1 + B2*X2",
algorithm='QR',
coeff_stats=True,
model_stats=True,
residuals=True)
# Example 1: Perform Breusch-Pagan-Godfrey (BPG) test using input as teradataml
# TDSeries object generated from model residuals.
# Extract residuals from the model as TDSeries.
data_series_bg = TDSeries(data=mvr_out.fitresiduals,
id="cityid",
row_index="ROW_I",
row_index_style= "SEQUENCE",
payload_field=["RESIDUAL","ACTUAL_VALUE","CALC_VALUE"],
payload_content="MULTIVAR_REAL")
uaf_out = BreuschPaganGodfrey(data=data_series_bg,
variables_count=2,
significance_level=0.01)
# Print the result DataFrame.
print(uaf_out.result)
# Example 2: Perform Koenker studentized version of the Breusch-Pagan-Godfrey
# (BPG) test using input as teradataml TDAnalyticResult object
# generated from model output.
# Create teradataml TDAnalyticResult object from model output.
data_art_df = TDAnalyticResult(data=mvr_out.result)
uaf_out = BreuschPaganGodfrey(data=data_art_df,
variables_count=2,
studentize=True,
significance_level=0.01)
# Print the result DataFrame.
print(uaf_out.result)
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