verticapy.vDataFrame.regr¶
- vDataFrame.regr(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, method: Literal['avgx', 'avgy', 'count', 'intercept', 'r2', 'slope', 'sxx', 'sxy', 'syy', 'beta', 'alpha'] = 'r2', show: bool = True, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure¶
Calculates the regression matrix for the given vDataFrame. This matrix is essential in regression analysis, enabling the modeling of relationships between variables and predicting outcomes. It plays a crucial role in understanding how independent variables influence the dependent variable, which can be invaluable for various data analysis and modeling tasks.
Parameters¶
- columns: SQLColumns, optional
List of the vDataColumns names. If empty, all numerical vDataColumns are used.
method: str, optional
Method to use to compute the regression matrix.
- avgx:
Average of the independent expression in an expression pair.
- avgy:
Average of the dependent expression in an expression pair.
- count:
Count of all rows in an expression pair.
- alpha:
Intercept of the regression line determined by a set of expression pairs.
- r2:
Square of the correlation coefficient of a set of expression pairs.
- beta:
Slope of the regression line, determined by a set of expression pairs.
- sxx:
Sum of squares of the independent expression in an expression pair.
- sxy:
Sum of products of the independent expression multiplied by the dependent expression in an expression pair.
- syy:
Returns the sum of squares of the dependent expression in an expression pair.
- show: bool, optional
If set to True, the Plotting object is returned.
- chart: PlottingObject, optional
The chart object used to plot.
- **style_kwargs
Any optional parameter to pass to the plotting functions.
Returns¶
- obj
Plotting Object.
Examples¶
Import VerticaPy.
import verticapy as vp
Import numpy to create a random dataset.
import numpy as np
Generate a dataset using the following data.
N = 30 # Number of records data = vp.vDataFrame( { "score1": np.random.normal(5, 1, N), "score2": np.random.normal(8, 1.5, N), "score3": np.random.normal(10, 2, N), "score4": np.random.normal(14, 3, N), } )
Draw the regression matrix using the Beta coefficient.
data.regr(method = "beta")
Draw the regression matrix using the Alpha coefficient.
data.regr(method = "alpha")
Draw the regression matrix using the R2 correlation coefficient.
data.regr(method = "r2")