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Table 3 Parameter estimates from the linear mixed models fitted by Bayesian Markov chain Monte Carlo methods

From: Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

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)
  1. aStandard deviation.