July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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426
13
章
Python
におけるモデリングライブラリ入門
●
線形モデル、一般化線形モデル、ロバスト線形モデル
●
線形混合効果モデル
●
分散分析
●
時系列分析と状態空間モデル
●
一般化モーメント法
次の節以降では、
statsmodels
のいくつかの基礎的なツールを使い、
Patsy
の
formula
式と
pandas
の
DataFrame
を使ったモデリングインタフェースの使用法を見ていきます。
13.3.1
線形モデルの推定
statsmodels
には、さまざまな線形回帰モデルがあります。基本的なもの（最小二乗法、
OLS
）から、
より複雑なもの（反復再重み付け最小二乗法、
IRLS
）まであります。
statsmodels
の線形モデルには、
2
つの主なインタフェースがあります。配列ベースのものと、
formula
式ベースのものです。これらはそれぞれ次の
API
モジュールをインポートすれば使用できます。
import statsmodels.api as sm
import st
atsmodels.formula.api as smf
これらの使い方を見るために、ランダムなデータから線形モデルを
1
つ作ってみましょう。
def dnorm(mean, variance, size=1):
if isinstance(size, int):
size = size,
return mean + np.sqrt(variance) * np.random.randn(*size)
#
再現性のために乱数のシードを指定する ...
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