Table 3 Parameter estimates from the linear mixed models fitted by Bayesian Markov chain Monte Carlo methods
Model | Fixed effects | Random effects | Log-linear variance | ||
|---|---|---|---|---|---|
Intercept, β 0 (SD a ) | Slope, β 1 (SD) | Covariance matrix, Σ (SD) | exp(intercept), σ 2 (SD) | Slope, γ (SD) | |
1 | 28.7 (0.6) | −3.5 (0.4) | \( \left[\begin{array}{cc}\hfill 0\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill 0.1\kern0.5em (0.3)\hfill \end{array}\right] \) | 23.3 (4.1) | 0 |
2 | 28.7 (1.2) | −3.5 (0.2) | \( \left[\begin{array}{cc}\hfill 16.8\;(9.9)\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill 0\hfill \end{array}\right] \) | 10 (1.9) | 0 |
3 | 28.7 (1.3) | −3.5 (0.3) | \( \left[\begin{array}{cc}\hfill 17.1\kern0.22em (10.1)\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill 0.3\;(0.4)\hfill \end{array}\right] \) | 9.3 (1.9) | 0 |
4 | 28.7 (1.2) | −3.5 (0.3) | \( \left[\begin{array}{cc}\hfill 14.6\kern0.22em (8.0)\hfill & \hfill -2.5\kern0.5em (1.6)\hfill \\ {}\hfill -2.5\kern0.5em (1.6)\hfill & \hfill 0.5(0.4)\hfill \end{array}\right] \) | 9.0 (1.7) | 0 |
5 | 28.1 (1.0) | −3.0 (0.2) | \( \left[\begin{array}{cc}\hfill 12.1\kern0.22em (6.8)\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill 0\hfill \end{array}\right] \) | 0.0 (0.0) | 0.2 (0.0) |
6 | 28.2 (1.1) | −3.1 (0.2) | \( \left[\begin{array}{cc}\hfill 14.0\kern0.22em (8.0)\hfill & \hfill 0\hfill \\ {}\hfill 0\hfill & \hfill 0.2\;(0.2)\hfill \end{array}\right] \) | 0.0 (0.0) | 0.2 (0.0) |
7 | 28.4 (1.1) | −3.3 (0.2) | \( \left[\begin{array}{cc}\hfill 14.2\kern0.22em (8.3)\hfill & \hfill -1.4\kern0.5em (1.3)\hfill \\ {}\hfill -1.4\kern0.5em (1.3)\hfill & \hfill 0.2\;(0.2)\hfill \end{array}\right] \) | 0.0 (0.0) | 0.2 (0.0) |