Specify the cycle threshold adjustment model formula
Usage
adjustment_formula(formula = ~1, obs, beta_default = c(0, 0.1))Arguments
- formula
A model formula defaulting to
~1.- obs
A data.frame with the following variables:
id: An integer vector uniquely identifying eahc infection.test_id: An integer vector uniquely identiying each testct_value: Numeric cycle threshold value.t: Relative (to a baseline) time of the test yielding a Ct value.t_rel_uncensored: Time of test relative to the first uncensored Ct value for that id.onset_t: Relative (to a baseline) time of onset for each infectiononset_t_rel_uncensored: Time of onset relative to the first uncensored Ct value for that id. (optional). NA if unavailable/asymptomatic.censored: Logical, indicating if the Ct has been censored.
- beta_default
A vector of length two containing the default mean and standard deviation to use as the prior for all covariate effect sizes.
Value
A named list including the design matrix ("design")
and a data.table of priors for covariate effects ("beta").
See also
Functions used to design and setup models
epict_formula_as_list(),
epict_individual_priors_as_list(),
epict_inference_opts(),
epict_model_opts(),
epict_obs_as_list(),
epict_onset_obs_as_list(),
epict_population_priors_as_list(),
epict_posterior_as_prior(),
piecewise_formula(),
select_piecewise_parameters()
Examples
obs <- data.frame(
age = c(1, 2, 3), cats = c(1, 2, 1), status = c("h", "l", "h")
)
adjustment_formula(~ cats + age + status, obs)
#> $design
#> (Intercept) cats age statusl
#> 1 1 1 1 0
#> 2 1 2 2 1
#> 3 1 1 3 0
#> attr(,"assign")
#> [1] 0 1 2 3
#> attr(,"contrasts")
#> attr(,"contrasts")$status
#> [1] "contr.treatment"
#>
#>
#> $beta
#> parameter effect mean sd
#> 1: ct_scale age 0 0.1
#> 2: ct_scale cats 0 0.1
#> 3: ct_scale statusl 0 0.1
#> 4: ct_shift age 0 0.1
#> 5: ct_shift cats 0 0.1
#> 6: ct_shift statusl 0 0.1
#>