R/observation_models.R
observation_model_continuous_bounded.RdThis observation model observes the latent biomarker quantities given a continuous assay with user-specified lower and upper limits and no added noise.
observation_model_continuous_bounded(biomarker_states, model_pars, bounds, ...)tibble containing true biomarker quantities for all individuals across all time steps and biomarkers. Variables should include: 1) i: the individual ID; 2) t: the time period; 3) b: the biomarker ID; 4) value: the latent biomarker quantity for the given i, t and b
a tibble containing information for all parameters needed to simulate the observation process. This should usually contain: 1) exposure_id: numeric exposure ID; 2) biomarker_id: numeric biomarker ID; 3) name: the character name of the parameter; 4) mean: numeric mean of this parameter distribution; 5) sd: the numeric standard deviation of the parameter distribution
a tibble containing the assay lower bound and upper bound for all biomarkers; column namesare 1) biomarker_id; 2) name; 3) value, where name is either lower_bound or upper_bound
Additional arguments
biomarker_states is returned with a new column, observed, for observed biomarker quantities
Other observation_model:
observation_model_continuous_bounded_noise(),
observation_model_continuous_noise(),
observation_model_continuous(),
observation_model_discrete_noise(),
observation_model_discrete()
bounds <- dplyr::tibble(biomarker_id=1,name=c("lower_bound","upper_bound"),value=c(2,8))
observation_model_continuous_bounded(example_biomarker_states, NULL,bounds)
#> i t b value observed
#> 1: 1 1 1 NA NA
#> 2: 1 2 1 NA NA
#> 3: 1 3 1 NA NA
#> 4: 1 4 1 NA NA
#> 5: 1 5 1 NA NA
#> ---
#> 11996: 100 116 1 NA NA
#> 11997: 100 117 1 3.835267 3.835267
#> 11998: 100 118 1 3.823634 3.823634
#> 11999: 100 119 1 3.812000 3.812000
#> 12000: 100 120 1 3.800367 3.800367