risk_continuous: Risk model for continuous outcome
In sachsmc/pseval: Methods for Evaluating Principal Surrogates of Treatment Response
Description
This model assumes that the outcome Y is normally distributed conditional on
S.1 and Z, with mean determined by the model formula. It also assumes that
larger values of Y are more indicative of poor outcomes, e.g., blood
pressure.
Usage
1
risk_continuous(model = Y ~ S.1 * Z, D = 5000)
Arguments
model
Formula specifying the risk model for the mean
D
number of samples for the simulated annealing integration
sachsmc/pseval documentation built on May 29, 2019, 12:55 p.m.
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Description
This model assumes that the outcome Y is normally distributed conditional on S.1 and Z, with mean determined by the model formula. It also assumes that larger values of Y are more indicative of poor outcomes, e.g., blood pressure.
Usage
1 | risk_continuous(model = Y ~ S.1 * Z, D = 5000)
|
Arguments
model |
Formula specifying the risk model for the mean |
D |
number of samples for the simulated annealing integration |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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