View source: R/bayesianMixtureFit.R
Creates a PriorAndInitsMixture object. These are guesses of the original parameters based on (protocol) assumptions.
1 2 | PriorAndInitValues(shape.init, shape.min, shape.max, ctrl.median, hr,
mixture.min, mixture.max, scale.sd.min = 0.9, scale.sd.max = 1.1)
|
shape.init |
Initial value of the shape parameter. |
shape.min |
The shape parameter prior is uniformly distributed between [shape.min, shape.max]. |
shape.max |
The shape parameter prior is uniformly distributed between [shape.min, shape.max]. |
ctrl.median |
Control median [months] |
hr |
Hazard ratio |
mixture.min |
The minimum mixture coefficient, e.g. randomization balance (boundary of uniform distribution), prior is uniformly distributed between [mixture.min, mixture.max] |
mixture.max |
The maximum mixture coefficient, e.g. randomization balance (boundary of uniform distribution), prior is uniformly distributed between [mixture.min, mixture.max] |
scale.sd.min |
The minimum standard deviation of the log(scale) (0.5), prior is uniformly distributed between [scale.sd.min, scale.sd.max] |
scale.sd.max |
The maximum standard deviation of the log(scale) (2), prior is uniformly distributed between [scale.sd.min, scale.sd.max] |
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