Регрессия Пуассона в статистических моделях и R

Учитывая некоторые случайно сгенерированные данные с

2 столбца,
50 строк и
целочисленный диапазон между 0-100

С помощью R можно построить планшета и график диагностики как таковой:

> col=2
> row=50
> range=0:100
> df <- data.frame(replicate(col,sample(range,row,rep=TRUE)))
> model <- glm(X2 ~ X1, data = df, family = poisson)
> glm.diag.plots(model)

В Python это даст мне прогноз строки и остаточный график:

import numpy as np
import pandas as pd
import statsmodels.formula.api
from statsmodels.genmod.families import Poisson
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.randint(100, size=(50,2)))
df.rename(columns={0:'X1', 1:'X2'}, inplace=True)
glm = statsmodels.formula.api.gee
model = glm("X2 ~ X1", groups=None, data=df, family=Poisson())
results = model.fit()

И для построения диагностики в Python:

model_fitted_y = results.fittedvalues  # fitted values (need a constant term for intercept)
model_residuals = results.resid # model residuals
model_abs_resid = np.abs(model_residuals)  # absolute residuals


plot_lm_1 = plt.figure(1)
plot_lm_1.set_figheight(8)
plot_lm_1.set_figwidth(12)
plot_lm_1.axes[0] = sns.residplot(model_fitted_y, 'X2', data=df, lowess=True, scatter_kws={'alpha': 0.5}, line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})
plot_lm_1.axes[0].set_xlabel('Line Predictor')
plot_lm_1.axes[0].set_ylabel('Residuals')
plt.show()

Но когда я пытаюсь получить статистику повара,

# cook distance, from statsmodels internals
model_cooks = results.get_influence().cooks_distance[0]

он высказал ошибку:

AttributeError                            Traceback (most recent call last)
<ipython-input-66-0f2bedfa1741> in <module>()
      4 model_residuals = results.resid
      5 # normalized residuals
----> 6 model_norm_residuals = results.get_influence().resid_studentized_internal
      7 # absolute squared normalized residuals
      8 model_norm_residuals_abs_sqrt = np.sqrt(np.abs(model_norm_residuals))

/opt/conda/lib/python3.6/site-packages/statsmodels/base/wrapper.py in __getattribute__(self, attr)
     33             pass
     34 
---> 35         obj = getattr(results, attr)
     36         data = results.model.data
     37         how = self._wrap_attrs.get(attr)

AttributeError: 'GEEResults' object has no attribute 'get_influence'

Есть ли способ отобразить все 4 диагностических графика в Python, как в R?

Как получить статистику по повару готовой модели в Python с помощью statsmodels?

import pandas as pd import statsmodels.api as sm # Read data generated in R using pandas or something similar df = pd.read_csv(...) # file name goes here # Add a column of ones for the intercept to create input X X = np.column_stack( (np.ones((df.shape[0], 1)), df.X1) ) # Relabel dependent variable as y (standard notation) y = df.X2 # Fit GLM in statsmodels using Poisson link function sm.GLM(y, X, family = Poisson()).fit().summary()

from __future__ import division, print_function import numpy as np import pandas as pd import statsmodels.api as sm PATH = '/Users/robertmilletich/test_reg.csv' def _weight_matrix(fitted_model): """Calculates weight matrix in Poisson regression Parameters ---------- fitted_model : statsmodel object Fitted Poisson model Returns ------- W : 2d array-like Diagonal weight matrix in Poisson regression """ return np.diag(fitted_model.fittedvalues) def _hessian(X, W): """Hessian matrix calculated as -X'*W*X Parameters ---------- X : 2d array-like Matrix of covariates W : 2d array-like Weight matrix Returns ------- hessian : 2d array-like Hessian matrix """ return -np.dot(X.T, np.dot(W, X)) def _hat_matrix(X, W): """Calculate hat matrix = W^(1/2) * X * (X'*W*X)^(-1) * X'*W^(1/2) Parameters ---------- X : 2d array-like Matrix of covariates W : 2d array-like Diagonal weight matrix Returns ------- hat : 2d array-like Hat matrix """ # W^(1/2) Wsqrt = W**(0.5) # (X'*W*X)^(-1) XtWX = -_hessian(X = X, W = W) XtWX_inv = np.linalg.inv(XtWX) # W^(1/2)*X WsqrtX = np.dot(Wsqrt, X) # X'*W^(1/2) XtWsqrt = np.dot(X.T, Wsqrt) return np.dot(WsqrtX, np.dot(XtWX_inv, XtWsqrt)) def main(): # Load data and separate into X and y df = pd.read_csv(PATH) X = np.column_stack( (np.ones((df.shape[0], 1)), df.X1 ) ) y = df.X2 # Fit model model = sm.GLM(y, X, family=sm.families.Poisson()).fit() # Weight matrix W = _weight_matrix(model) # Hat matrix H = _hat_matrix(X, W) hii = np.diag(H) # Diagonal values of hat matrix # Pearson residuals r = model.resid_pearson # Cook distance (formula used by R = (res/(1 - hat))^2 * hat/(dispersion * p)) # Note: dispersion is 1 since we aren't modeling overdispersion cooks_d = (r/(1 - hii))**2 * hii/(1*2) if __name__ == "__main__": main()

Ответ 1