二元配置のモデル
因子が2つあるモデルを二元配置という.
二元配置
とある合金の製造で,温度と触媒の量で延性がどのように変わるかを知りたいとき,温度の水準を\(A_i, i \leq I\),触媒の水準を\(B_j, j \leq J\),繰り返し回数を\(K\)とする.
| 因子\(A\) | 因子\(B\) | 繰り返し | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | \(\cdots\) | \(j\) | \(\cdots\) | \(J\) | ||
| \(A_1\) | \(B_1\) | \(y_{111}\) | \(y_{112}\) | \(\cdots\) | \(y_{11k}\) | \(\cdots\) | \(y_{11K}\) |
| \(B_2\) | \(y_{121}\) | \(y_{122}\) | \(\cdots\) | \(y_{12k}\) | \(\cdots\) | \(y_{12K}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_j\) | \(y_{1j1}\) | \(y_{1j2}\) | \(\cdots\) | \(y_{1jk}\) | \(\cdots\) | \(y_{1jK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_J\) | \(y_{1J1}\) | \(y_{1J2}\) | \(\cdots\) | \(y_{1Jk}\) | \(\cdots\) | \(y_{1JK}\) | |
| \(A_2\) | \(B_1\) | \(y_{211}\) | \(y_{212}\) | \(\cdots\) | \(y_{21k}\) | \(\cdots\) | \(y_{21K}\) |
| \(B_2\) | \(y_{221}\) | \(y_{222}\) | \(\cdots\) | \(y_{22k}\) | \(\cdots\) | \(y_{22K}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_j\) | \(y_{2j1}\) | \(y_{2j2}\) | \(\cdots\) | \(y_{2jk}\) | \(\cdots\) | \(y_{2jK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_J\) | \(y_{2J1}\) | \(y_{2J2}\) | \(\cdots\) | \(y_{2Jk}\) | \(\cdots\) | \(y_{2JK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) |
| \(A_i\) | \(B_1\) | \(y_{i11}\) | \(y_{i12}\) | \(\cdots\) | \(y_{i1k}\) | \(\cdots\) | \(y_{i1K}\) |
| \(B_2\) | \(y_{i21}\) | \(y_{i22}\) | \(\cdots\) | \(y_{i2k}\) | \(\cdots\) | \(y_{i2K}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_j\) | \(y_{ij1}\) | \(y_{ij2}\) | \(\cdots\) | \(y_{ijk}\) | \(\cdots\) | \(y_{ijK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_J\) | \(y_{iJ1}\) | \(y_{iJ2}\) | \(\cdots\) | \(y_{iJk}\) | \(\cdots\) | \(y_{iJK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(A_I\) | \(B_1\) | \(y_{I11}\) | \(y_{I12}\) | \(\cdots\) | \(y_{I1k}\) | \(\cdots\) | \(y_{I1K}\) |
| \(B_2\) | \(y_{I21}\) | \(y_{I22}\) | \(\cdots\) | \(y_{I2k}\) | \(\cdots\) | \(y_{I2K}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_j\) | \(y_{Ij1}\) | \(y_{Ij2}\) | \(\cdots\) | \(y_{Ijk}\) | \(\cdots\) | \(y_{IjK}\) | |
| \(\vdots\) | \(\vdots\) | \(\vdots\) | \(\ddots\) | \(\vdots\) | \(\vdots\) | \(\vdots\) | |
| \(B_J\) | \(y_{IJ1}\) | \(y_{IJ2}\) | \(\cdots\) | \(y_{IJk}\) | \(\cdots\) | \(y_{IJK}\) | |
因子(A)と因子(B)には交互作用があるかもしれない.なので,交互作用を\(A \times B\)と表示する.
構造モデルは \[ Y_{ijk} = \mu + \alpha_{i} + \beta_{j} + (\alpha\beta)_{ij} + E_{ijk}, E_{ijk} \sim \mathcal{N}(0, \sigma^2) \\ \sum^{I}_{i=1} \alpha_i = 0 \\ \sum^{J}_{j=1} \beta_j = 0 \\ \sum^{I}_{i=1} (\alpha\beta)_{ij} = \sum^{J}_{j=1} (\alpha\beta)_{ij} = 0 \] で,\((\alpha\beta)_{ij}\)は交互作用の効果を表す.
各平方和は, \[ SS_T = \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(Y_{ijk} - \bar{Y}_{…})^2 \]
\[ \begin{align} SS_A &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{i..} - \bar{Y}_{…})^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\alpha_{i} + \bar{E}_{i..} - \bar{E}_{…})^2 \\ &= JK\sum^{I}_{i=1}(\alpha_{i} + \bar{E}_{i..} - \bar{E}_{…})^2 \end{align} \]
\[ \begin{align} SS_B &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{.j.} - \bar{Y}_{…})^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\beta_{j} + \bar{E}_{.j.} - \bar{E}_{…})^2 \\ &= IK\sum^{J}_{j=1}(\beta_{j} + \bar{E}_{.j.} - \bar{E}_{…})^2 \\ \end{align} \]
\[ \begin{align} SS_{A \times B} &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{ij.} - \bar{Y}_{i..} - \bar{Y}_{.j.} + \bar{Y}_{…})^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}((\alpha\beta)_{ij} + \bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2 \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1}((\alpha\beta)_{ij} + \bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2 \end{align} \]
\[ \begin{align} SS_{E} &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{ijk} - \bar{Y}_{ij.})^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\mu + \alpha_{i} + \beta_{j} + (\alpha\beta)_{ij} + E_{ijk} - (\mu + \alpha_{i} + \beta_{j} + (\alpha\beta)_{ij} + \bar{E}_{ij.}))^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(E_{ijk} - \bar{E}_{ij.})^2 \end{align} \]
\[ \begin{align} SS_T &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(Y_{ijk} - \bar{Y}_{ij.} + \bar{Y}_{ij.} + \bar{Y}_{i..} - \bar{Y}_{i..} + \bar{Y}_{.j.} - \bar{Y}_{.j.} + \bar{Y}_{…} - \bar{Y}_{…} - \bar{Y}_{…})^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}((Y_{i..} - Y_{…}) + (Y_{.j.} - Y_{…}) + (\bar{Y}_{ij.} - Y_{i..} - Y_{.j.} + \bar{Y}_{…}) + (\bar{Y}_{ijk} - \bar{Y}_{ij.}))^2 \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(Y_{i..} - Y_{…})^2 + \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(Y_{.j.} - Y_{…})^2 + \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{ij.} - Y_{i..} - Y_{.j.} + \bar{Y}_{…})^2 + \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\bar{Y}_{ijk} - \bar{Y}_{ij.})^2 \\ &= SS_A+SS_B+SS_{A \times B} + SS_E \end{align} \]
それぞれの平均を取ると, \[ \begin{align} \mathrm{E}[SS_A] &= \mathrm{E}[(\alpha_{i} + \bar{E}_{i..} - \bar{E}_{…})^2] \\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\mathrm{E}[(\bar{E}_{i..} - \bar{E}_{…})^2] \\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\mathrm{V}[\bar{E}_{i..} - \bar{E}_{…}] \\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\{\mathrm{V}[\bar{E}_{i..}] + \mathrm{V}[\bar{E}_{…}] - 2 \mathrm{Cov}(\bar{E}_{i..}, \bar{E}_{…})\}\\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\Bigl\{\frac{\sigma^2}{JK} + \frac{\sigma^2}{IJK} - 2 \mathrm{Cov}\Bigl(\bar{E}_{i..}, \frac{1}{I} \sum^{I}_{k=1} \bar{E}_{k..}\Bigr) \Bigr\} \\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\Bigl\{\frac{\sigma^2}{JK} + \frac{\sigma^2}{IJK} - 2 \frac{1}{I} \mathrm{V}[\bar{E}_{i..}] \Bigr\}\\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + JK\sum^{I}_{i=1}\Bigl\{\frac{\sigma^2}{JK} + \frac{\sigma^2}{IJK} - 2 \frac{\sigma^2}{IJK}\Bigr\}\\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + \sum^{I}_{i=1}\Bigl(\sigma^2 + \frac{\sigma^2}{I} - 2 \frac{\sigma^2}{I}\Bigr)\\ &= JK\sum^{I}_{i=1}\alpha_{i}^2 + (I-1)\sigma^2 \end{align} \]
\[ \begin{align} \mathrm{E}[SS_B] &= \mathrm{E}[(\beta_{j} + \bar{E}_{.j.} - \bar{E}_{…})^2] \\ &= IK\sum^{J}_{i=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\mathrm{E}[(\bar{E}_{.j.} - \bar{E}_{…})^2] \\ &= IK\sum^{J}_{i=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\mathrm{V}[\bar{E}_{.j.} - \bar{E}_{…}] \\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\{\mathrm{V}[\bar{E}_{.j.}] + \mathrm{V}[\bar{E}_{…}] - 2 \mathrm{Cov}(\bar{E}_{.j.}, \bar{E}_{…})\}\\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\Bigl\{\frac{\sigma^2}{IK} + \frac{\sigma^2}{IJK} - 2 \mathrm{Cov}\Bigl(\bar{E}_{.j.}, \frac{1}{J} \sum^{J}_{k=1} \bar{E}_{.k.}\Bigr) \Bigr\} \\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\Bigl\{\frac{\sigma^2}{IK} + \frac{\sigma^2}{IJK} - 2 \frac{1}{I} \mathrm{V}[\bar{E}_{.j.}] \Bigr\}\\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + IK\sum^{J}_{j=1}\Bigl\{\frac{\sigma^2}{IK} + \frac{\sigma^2}{IJK} - 2 \frac{\sigma^2}{IJK}\Bigr\}\\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + \sum^{J}_{j=1}\Bigl(\sigma^2 + \frac{\sigma^2}{J} - 2 \frac{\sigma^2}{J}\Bigr)\\ &= IK\sum^{J}_{j=1}\beta_{j}^2 + (J-1)\sigma^2 \end{align} \]
\[ \begin{align} \mathrm{E}[SS_{A \times B}] &= \mathrm{E}[K\sum^{I}_{i=1}\sum^{J}_{j=1}((\alpha\beta)_{ij} + \bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2] \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} \mathrm{E}[(\alpha\beta)_{ij} + \bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2] \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} \mathrm{E}[{(\alpha\beta)_{ij}}^2 + 2(\alpha\beta)_{ij}(\bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…}) + (\bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2] \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + K\sum^{I}_{i=1}\sum^{J}_{j=1} \mathrm{E}[(\bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…})^2] \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + K\sum^{I}_{i=1}\sum^{J}_{j=1} \mathrm{V}[\bar{E}_{ij.} - \bar{E}_{i..} - \bar{E}_{.j.} + \bar{E}_{…}] \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + K\sum^{I}_{i=1}\sum^{J}_{j=1} \{ \mathrm{V}[\bar{E}_{ij.}] + \mathrm{V}[\bar{E}_{i..}] + \mathrm{V}[\bar{E}_{.j.}] + \mathrm{V}[\bar{E}_{…}] - 2 \mathrm{Cov}[\bar{E}_{ij.},\bar{E}_{i..}] - 2 \mathrm{Cov}[\bar{E}_{ij.},\bar{E}_{.j.}] + 2 \mathrm{Cov}[\bar{E}_{ij.},\bar{E}_{…}] + 2 \mathrm{Cov}[\bar{E}_{i..},\bar{E}_{.j.}] - 2 \mathrm{Cov}[\bar{E}_{i..},\bar{E}_{…}] - 2 \mathrm{Cov}[\bar{E}_{.j.},\bar{E}_{…}] \} \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + K\sum^{I}_{i=1}\sum^{J}_{j=1} \{ \frac{\sigma^2}{K} + \frac{\sigma^2}{JK} + \frac{\sigma^2}{IK} + \frac{\sigma^2}{IJK} - 2 \frac{1}{J}\frac{\sigma^2}{K} - 2 \frac{1}{I}\frac{\sigma^2}{K} + 2 \frac{1}{IJ}\frac{\sigma^2}{K} + 2 \frac{1}{IJ}\frac{\sigma^2}{K} - 2 \frac{1}{I} \frac{\sigma^2}{JK} - 2 \frac{1}{IK}\frac{\sigma^2}{J} \} \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + (IJ+I+J+1-2I-2J+2+2-2-2)\sigma^2 \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + (IJ+1-I-J)\sigma^2 \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + (I(J-1)-(J-1))\sigma^2 \\ &= K\sum^{I}_{i=1}\sum^{J}_{j=1} {(\alpha\beta)_{ij}}^2 + (I-1)(J-1)\sigma^2 \\ \end{align} \]
\[ \begin{align} \mathrm{E}[SS_{E}] &= \mathrm{E}[\sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(E_{ijk} - \bar{E}_{ij.})^2] \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}\mathrm{E}[(E_{ijk} - \bar{E}_{ij.})^2] \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}\mathrm{V}[E_{ijk} - \bar{E}_{ij.}] \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\mathrm{V}[E_{ijk}] + \mathrm{V}[\bar{E}_{ij.}] - 2\mathrm{Cov}[E_{ijk}, \bar{E}_{ij.}])\\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\sigma^2 + \frac{\sigma^2}{K} - 2\frac{1}{K}\sigma^2) \\ &= \sum^{I}_{i=1}\sum^{J}_{j=1}\sum^{K}_{k=1}(\sigma^2 - \frac{\sigma^2}{K}) \\ &= IJK\sigma^2 - IJ\sigma^2 \\ &= IJ(K-1)\sigma^2 \end{align} \]
これまでと同様に,各因子の効果がない場合と考え,各平均平方和は, \[ MS_A = \frac{SS_A}{I-1} \\ MS_B = \frac{SS_B}{J-1} \\ MS_{A \times B} = \frac{SS_{A \times B}}{(I-1)(J-1)} \\ MS_{E} = \frac{SS_E}{IJ(K-1)} \] で, \[ \frac{(I-1)MS_A}{\sigma^2} \sim \chi^2_{I-1} \\ \frac{(J-1)MS_B}{\sigma^2} \sim \chi^2_{J-1} \\ \frac{(I-1)(J-1)MS_{A \times B}}{\sigma^2} \sim \chi^2_{(I-1)(J-1)} \\ \frac{IJ(K-1)MS_{E}}{\sigma^2} \sim \chi^2_{IJ(K-1)} \] なので,検定統計量は, \[ \frac{MS_A}{MS_E} \sim F_{I-1,IJ(K-1)}\\ \frac{MS_B}{MS_E} \sim F_{J-1,IJ(K-1)}\\ \frac{MS_{A \times B}}{MS_E} \sim F_{(I-1)(J-1),IJ(K-1)}\\ \]