我有一些按年记录的数据如下所示。
mydata = [0.6619346141815186, 0.7170140147209167, 0.692265510559082, 0.6394098401069641, 0.6030995845794678, 0.6500746607780457, 0.6013327240943909, 0.6273292303085327, 0.5865356922149658, 0.6477396488189697, 0.5827181339263916, 0.6496025323867798, 0.6589270234107971, 0.5498126149177551, 0.48638370633125305, 0.5367399454116821, 0.517595648765564, 0.5171639919281006, 0.47503289580345154, 0.6081966757774353, 0.5808742046356201, 0.5856912136077881, 0.5608134269714355, 0.6400936841964722, 0.6766082644462585]corresponding_year = [1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994]]我使用statsmodels Python包计算lowess，如下所示。
import statsmodels.api as smlowess = sm.nonparametric.lowessz = lowess(x, y, frac= 1./3, it=3)我得到的输出如下所示。
[[1.96000000e+03, 6.95703548e-01], [1.96100000e+03, 6.81750671e-01], [1.96200000e+03, 6.68002318e-01], [1.96300000e+03, 6.55138324e-01], [1.96400000e+03, 6.38960761e-01], [1.96500000e+03, 6.25042177e-01], [1.96600000e+03, 6.18586936e-01], [1.96700000e+03, 6.17026334e-01], [1.96800000e+03, 6.14565102e-01], [1.96900000e+03, 6.17610340e-01], [1.97000000e+03, 6.20404414e-01], [1.97100000e+03, 6.10193222e-01], [1.97200000e+03, 5.90100648e-01], [1.97300000e+03, 5.70935248e-01], [1.97400000e+03, 5.47818726e-01], [1.97500000e+03, 5.25788570e-01], [1.97600000e+03, 5.18661218e-01], [1.97700000e+03, 5.28921300e-01], [1.97800000e+03, 5.42783400e-01], [1.97900000e+03, 5.55425915e-01], [1.98000000e+03, 5.71486587e-01], [1.98100000e+03, 5.91539778e-01], [1.98200000e+03, 6.13021691e-01], [1.98300000e+03, 6.34508409e-01], [1.98400000e+03, 6.57703989e-01]]然而，我不清楚在statsmodel中我得到的两个值是什么。我是否做错了什么？此外，我还想知道frac和it这两个参数的作用是什么？
此外，我还想使用seaborn绘制平滑的时间序列。看起来seaborn支持lowess。然而，它没有frac和it参数。请看下面的代码。
import numpy as npimport seaborn as snsx = np.arange(0, 10, 0.01)ytrue = np.exp(-x / 5) + 2 * np.sin(x / 3)y = ytrue + np.random.normal(size=len(x))sns.regplot(x, y, lowess=True)在这种情况下，是否可以使用statmodels的输出在seaborn中绘制regplot？
如果需要，我很乐意提供更多细节。
回答：
lowess的结果可以如以下代码所示绘制。请注意，lowess()的第一个参数是y值（endog），第二个是x（exog）。默认结果中，z[:,0]是排序后的x值，z[:,1]是相应的估计y值。
import matplotlib.pyplot as pltimport statsmodels.api as smimport numpy as npmydata = [0.6619346141815186, 0.7170140147209167, 0.692265510559082, 0.6394098401069641, 0.6030995845794678, 0.6500746607780457, 0.6013327240943909, 0.6273292303085327, 0.5865356922149658, 0.6477396488189697, 0.5827181339263916, 0.6496025323867798, 0.6589270234107971, 0.5498126149177551, 0.48638370633125305, 0.5367399454116821, 0.517595648765564, 0.5171639919281006, 0.47503289580345154, 0.6081966757774353, 0.5808742046356201, 0.5856912136077881, 0.5608134269714355, 0.6400936841964722, 0.6766082644462585]corresponding_year = [1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994]x = np.array(corresponding_year)y = np.array(mydata)z = sm.nonparametric.lowess(y, x, frac= 1./3, it=3)plt.plot(x, y, color='dodgerblue')plt.plot(z[:,0], z[:,1], 'ro-')plt.show()
附注：要在同一张图上与seaborn的regplot进行比较，请按以下方式调用它：
sns.regplot(x, y, lowess=True, ax=plt.gca())