This function is used internally by epinowcast to replace
default model priors with users specified ones (restricted to
normal priors with specified mean and standard deviations). A common
use would be extracting the posterior from a previous epinowcast()
run (using summary(nowcast, type = fit)
) and using this a prior.
Arguments
- priors
A
data.frame
with the following variables:variable
,mean
,sd
describing normal priors. Priors in the appropriate format are returned byenw_reference()
as well as by other similar model specification functions.- custom_priors
A
data.frame
with the following variables:variable
,mean
,sd
describing normal priors. Priors in the appropriate format are returned byenw_reference()
as well as by other similar model specification functions. Priors in this data.frame replace the default priors. Note that currently vectorised prior names (i.e those of the formvariable[n]
will be treated asvariable
).
See also
Functions used to help convert models into the format required for stan
enw_formula_as_data_list()
,
enw_model()
,
enw_priors_as_data_list()
,
enw_sample()
,
remove_profiling()
,
write_stan_files_no_profile()
Examples
# Update priors from a data.frame
priors <- data.frame(variable = c("x", "y"), mean = c(1, 2), sd = c(1, 2))
custom_priors <- data.frame(variable = "x[1]", mean = 10, sd = 2)
enw_replace_priors(priors, custom_priors)
#> variable mean sd
#> 1: y 2 2
#> 2: x 10 2
# Update priors from a previous model fit
default_priors <- enw_reference(
distribution = "lognormal",
data = enw_example("preprocessed"),
)$priors
print(default_priors)
#> variable
#> 1: refp_mean_int
#> 2: refp_sd_int
#> 3: refp_mean_beta_sd
#> 4: refp_sd_beta_sd
#> description
#> 1: Log mean intercept for parametric reference date delay
#> 2: Log standard deviation for the parametric reference date delay
#> 3: Standard deviation of scaled pooled parametric mean effects
#> 4: Standard deviation of scaled pooled parametric sd effects
#> distribution mean sd
#> 1: Normal 1.0 1
#> 2: Zero truncated normal 0.5 1
#> 3: Zero truncated normal 0.0 1
#> 4: Zero truncated normal 0.0 1
fit_priors <- summary(
enw_example("nowcast"), type = "fit",
variables = c("refp_mean_int", "refp_sd_int", "sqrt_phi")
)
fit_priors
#> variable mean median sd mad q5
#> 1: refp_mean_int[1] 1.4065186 1.3627850 0.31867081 0.28263545 0.9784832
#> 2: refp_sd_int[1] 1.9488911 1.9301000 0.17462563 0.16212231 1.6974820
#> 3: sqrt_phi[1] 0.3253364 0.3242025 0.03123022 0.03085661 0.2758690
#> q20 q80 q95 rhat ess_bulk ess_tail
#> 1: 1.1410640 1.6335340 2.0573970 1.012034 341.9545 526.7013
#> 2: 1.7996520 2.0693360 2.2809700 1.008008 404.0572 428.0146
#> 3: 0.2998764 0.3513736 0.3794238 1.002608 1325.7827 735.9718
enw_replace_priors(default_priors, fit_priors)
#> variable
#> 1: refp_mean_beta_sd
#> 2: refp_sd_beta_sd
#> 3: refp_mean_int
#> 4: refp_sd_int
#> 5: sqrt_phi
#> description
#> 1: Standard deviation of scaled pooled parametric mean effects
#> 2: Standard deviation of scaled pooled parametric sd effects
#> 3: <NA>
#> 4: <NA>
#> 5: <NA>
#> distribution mean sd
#> 1: Zero truncated normal 0.0000000 1.00000000
#> 2: Zero truncated normal 0.0000000 1.00000000
#> 3: <NA> 1.4065186 0.31867081
#> 4: <NA> 1.9488911 0.17462563
#> 5: <NA> 0.3253364 0.03123022