durbin_watson¶

In [ ]:

durbin_watson(vdf: vDataFrame, 
              eps: str, 
              ts: str, 
              by: list = [])

Durbin-Watson test (autocorrelation of residuals).

Parameters¶

Name	Type	Optional	Description
vdf	vDataFrame	❌	input vDataFrame.
eps	str	❌	Input residual vcolumn.
ts	str	❌	vcolumn used as timeline. It will be to use to order the data. It can be a numerical or type date like (date, datetime, timestamp...) vcolumn.
by	list	✓	vcolumns used in the partition.

Returns¶

float : Durbin-Watson statistic.

Example¶

In [2]:

from verticapy import *
texas = vDataFrame("Texas")
texas.plot(ts = "date", columns = ["cases", "deaths"])

Out[2]:

<AxesSubplot:xlabel='"date"'>

In [3]:

from verticapy.learn.linear_model import LinearRegression

model = LinearRegression("Texas_deaths_lr")
model.drop()
model.fit(texas, X = ["cases"], y = "deaths")
texas = model.predict(texas, name = "deaths_pred")
texas["eps"] = texas["deaths"] - texas["deaths_pred"]

In [5]:

from verticapy.stats import durbin_watson
durbin_watson(texas, 
              eps = "eps", 
              ts = "date")

Out[5]:

0.0430492268402852