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Observation model for continuous assays with detection limits and no added noise

Source: R/observation_models.R
observation_model_continuous_bounded.Rd

This observation model observes the latent biomarker quantities given a continuous assay with user-specified lower and upper limits and no added noise.

Usage

observation_model_continuous_bounded(biomarker_states, model_pars, bounds, ...)

Arguments

biomarker_states

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

model_pars

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

bounds

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

Value

biomarker_states is returned with a new column, observed, for observed biomarker quantities

See also

Other observation_model: observation_model_continuous_bounded_noise(), observation_model_continuous_noise(), observation_model_continuous(), observation_model_discrete_noise(), observation_model_discrete()

Examples

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  0.00000        2
#>     2:   1   2 1  0.00000        2
#>     3:   1   3 1  0.00000        2
#>     4:   1   4 1  0.00000        2
#>     5:   1   5 1  0.00000        2
#>    ---                            
#> 11996: 100 116 1 16.26414        8
#> 11997: 100 117 1 16.20296        8
#> 11998: 100 118 1 16.14178        8
#> 11999: 100 119 1 16.08060        8
#> 12000: 100 120 1 16.01942        8