evaluate_ppc: Set up posterior predictive checks
In JasperHG90/blm: Bayesian linear model
Description
Usage
Arguments
Value
Description
Set up posterior predictive checks
Usage
1
evaluate_ppc(x, return_all = TRUE, p = 1)
Arguments
x
blm object
return_all
logical. If TRUE, then the function will return the discrepancy statistics for the simulated and observed data.
p
proportion of samples used to compute posterior predictive checks. Defaults to p=1.
Value
prints summary of the ppc to the R console
JasperHG90/blm documentation built on Sept. 4, 2019, 11:16 a.m.
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Description Usage Arguments Value
Description
Set up posterior predictive checks
Usage
1 | evaluate_ppc(x, return_all = TRUE, p = 1)
|
Arguments
x |
blm object |
return_all |
logical. If TRUE, then the function will return the discrepancy statistics for the simulated and observed data. |
p |
proportion of samples used to compute posterior predictive checks. Defaults to p=1. |
Value
prints summary of the ppc to the R console
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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