These accessors return the current default priors used by mlumr(),
tagged with $default = TRUE and the package version. For
family = "normal" their scales are multiples of the IPD outcome SD
wherever the parameter is in outcome units; see prior_normal(). prior_summary()
prints the version so cross-release reproducibility is diagnosable: if a
later release changes a default, fits produced with an older version
will still carry the correct $version tag.
Usage
default_prior_intercept()
default_prior_beta()
default_prior_sigma()
default_prior_aux()
default_prior_smooth()Value
A prior list (see prior_normal()).
Details
default_prior_aux() and default_prior_smooth() apply to the survival
family only. prior_aux is a half-normal(0, 2) on the shape/scale
parameter(s) of parametric survival distributions (Weibull/Gompertz/gamma
shape, log-normal sdlog, generalized-gamma shapes). prior_smooth is a
half-normal(0, 1) on the random-walk smoothing SD of the M-spline /
piecewise-exponential baseline hazard.
Examples
default_prior_intercept()
#> $distribution
#> [1] "normal"
#>
#> $mean
#> [1] 0
#>
#> $sd
#> [1] 10
#>
#> $df
#> [1] NA
#>
#> $autoscale
#> [1] FALSE
#>
#> $default
#> [1] TRUE
#>
#> $version
#> [1] "0.1.0.9000"
#>
default_prior_beta()
#> $distribution
#> [1] "normal"
#>
#> $mean
#> [1] 0
#>
#> $sd
#> [1] 2.5
#>
#> $df
#> [1] NA
#>
#> $autoscale
#> [1] FALSE
#>
#> $default
#> [1] TRUE
#>
#> $version
#> [1] "0.1.0.9000"
#>
default_prior_sigma()
#> $distribution
#> [1] "normal"
#>
#> $mean
#> [1] 0
#>
#> $sd
#> [1] 2.5
#>
#> $df
#> [1] NA
#>
#> $autoscale
#> [1] FALSE
#>
#> $default
#> [1] TRUE
#>
#> $version
#> [1] "0.1.0.9000"
#>