example_antibody_data: Example antibody data
In seroanalytics/serosolver: Inference Framework for Serological Data
example_antibody_data R Documentation
Example antibody data
Description
Example of the antibody data frame generated by simulate_data, with matching infection history in example_inf_hist
Usage
data(example_antibody_data)
Format
A data frame with 4800 rows (each corresponding to a unique antibody measurement) and 6 variables:
- individual
index starting from 1 for the individual ID. This should be continuous from 1:n.
- sample_time
time the serum sample was taken, matching the time resolution used in the overall model.
- biomarker_id
ID of the biomarker being measured. Usually this would correspond to the time period in which an antigen was assumed to be circulating, e.g., an entry of 2015 would correspond to the variant assumed to be circulating throughout 2015.
- biomarker_group
Group ID of the biomarker being measured. This will usually just be 1, but can be used where there are multiple biomarker types (not just antigens/targets) per sample. For example, this might represent antibody levels and avidity.
- measurement
the antibody measurement for this biomarker ID at this sampling time.
- repeat_number
where there are repeat measurements, gives the repeat number for this serum sample and strain measured.
- population_group
group index of this individual, for example, corresponding to a study location.
- birth
date of birth matching the time resolution of the model.
See Also
Other example_data:
example_antigenic_map,
example_inf_chain,
example_inf_hist,
example_par_tab,
example_theta_chain
seroanalytics/serosolver documentation built on Dec. 14, 2024, 5:33 a.m.
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| example_antibody_data | R Documentation |
Example antibody data
Description
Example of the antibody data frame generated by simulate_data, with matching infection history in example_inf_hist
Usage
data(example_antibody_data)
Format
A data frame with 4800 rows (each corresponding to a unique antibody measurement) and 6 variables:
- individual
index starting from 1 for the individual ID. This should be continuous from 1:n.
- sample_time
time the serum sample was taken, matching the time resolution used in the overall model.
- biomarker_id
ID of the biomarker being measured. Usually this would correspond to the time period in which an antigen was assumed to be circulating, e.g., an entry of 2015 would correspond to the variant assumed to be circulating throughout 2015.
- biomarker_group
Group ID of the biomarker being measured. This will usually just be 1, but can be used where there are multiple biomarker types (not just antigens/targets) per sample. For example, this might represent antibody levels and avidity.
- measurement
the antibody measurement for this biomarker ID at this sampling time.
- repeat_number
where there are repeat measurements, gives the repeat number for this serum sample and strain measured.
- population_group
group index of this individual, for example, corresponding to a study location.
- birth
date of birth matching the time resolution of the model.
See Also
Other example_data:
example_antigenic_map,
example_inf_chain,
example_inf_hist,
example_par_tab,
example_theta_chain
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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