Python手册(Machine Learning)--statsmodels(Graphics)

---

Python手册(Machine Learning)–statsmodels(GettingStarted)
Python手册(Machine Learning)–statsmodels(Regression)
Python手册(Machine Learning)–statsmodels(ANOVA)
Python手册(Machine Learning)–statsmodels(Tables+Imputation)
Python手册(Machine Learning)–statsmodels(MultivariateStatistics)
Python手册(Machine Learning)–statsmodels(TimeSeries)
Python手册(Machine Learning)–statsmodels(Survival)
Python手册(Machine Learning)–statsmodels(Graphics)

---

拟合图

Goodness of Fit Plots	拟合图
gofplots.qqplot	QQ图。
gofplots.qqline	绘制一个qqplot的参考线
gofplots.qqplot_2samples	两个样本分位数的QQ图
gofplots.ProbPlot	自定义QQ图，PP图或概率图

>>> import statsmodels.api as sm
>>> from matplotlib import pyplot as plt
>>> import scipy.stats as stats
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> mod_fit = sm.OLS(data.endog, data.exog).fit()
>>> res = mod_fit.resid # residuals
>>> fig = sm.qqplot(res, stats.t, fit=True, line='45')
>>> plt.show()

箱线图

Boxplots	箱线图
boxplots.violinplot	在数据序列中为每个数据集制作小提琴图
boxplots.beanplot	在数据序列中创建每个数据集的bean图

>>> data = sm.datasets.anes96.load_pandas()
>>> party_ID = np.arange(7)
>>> labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
...           "Independent-Indpendent", "Independent-Republican",
...           "Weak Republican", "Strong Republican"]
>>> plt.rcParams['figure.subplot.bottom'] = 0.23  # keep labels visible
>>> age = [data.exog['age'][data.endog == id] for id in party_ID]
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> sm.graphics.violinplot(age, ax=ax, labels=labels,
...                        plot_opts={'cutoff_val':5, 'cutoff_type':'abs',
...                                   'label_fontsize':'small',
...                                   'label_rotation':30})
>>> ax.set_xlabel("Party identification of respondent.")
>>> ax.set_ylabel("Age")
>>> plt.show()

相关图

Correlation Plots	相关图
correlation.plot_corr	相关图
correlation.plot_corr_grid	创建相关图的网格
plot_grids.scatter_ellipse	用置信度椭圆创建一个散点图网格

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import statsmodels.graphics.api as smg
>>> hie_data = sm.datasets.randhie.load_pandas()
>>> corr_matrix = np.corrcoef(hie_data.data.T)
>>> smg.plot_corr(corr_matrix, xnames=hie_data.names)
>>> plt.show()

函数图

Functional Plots	函数图
functional.hdrboxplot	高密度区域箱线图
functional.fboxplot	绘制函数箱线图
functional.rainbowplot	为一组曲线创建一个彩虹图
functional.banddepth	计算一组函数曲线的带深度

>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> data = sm.datasets.elnino.load()
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> res = sm.graphics.hdrboxplot(data.raw_data[:, 1:],
...                              labels=data.raw_data[:, 0].astype(int),
...                              ax=ax)
>>> ax.set_xlabel("Month of the year")
>>> ax.set_ylabel("Sea surface temperature (C)")
>>> ax.set_xticks(np.arange(13, step=3) - 1)
>>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"])
>>> ax.set_xlim([-0.2, 11.2])
>>> plt.show()

回归图

Regression Plots	回归图
regressionplots.plot_fit	Plot fit against one regressor
regressionplots.plot_regress_exog	针对一个回归模型绘制回归结果。
regressionplots.plot_partregress	绘制对于单个回归模型的部分回归。
regressionplots.plot_ccpr	将CCPR与一位回归模型对比。
regressionplots.abline_plot	绘制斜线
regressionplots.influence_plot	回归影响
regressionplots.plot_leverage_resid2	Plots leverage statistics vs

时间序列图

Time Series Plots	时间序列图
tsaplots.plot_acf	绘制自相关函数
tsaplots.plot_pacf	绘制部分自相关函数
tsaplots.month_plot	每月数据的季节性
tsaplots.quarter_plot	季度数据的季节性

其他

Other Plots	其他
factorplots.interaction_plot	对每个因子水平的交互作用图
mosaicplot.mosaic	马赛克图
agreement.mean_diff_plot	Tukey’s Mean Difference Plot