transform_data

Generate a dataframe with `t`, `pos` and `tot` columns from
`t` and `seropositive` vectors.

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()).
The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

Anh Phan Truong Quynh

serosv

Model Infectious Disease Parameters from Serosurveys

Nguyen Pham Nguyen The

Long Bui Thanh

Tuyen Huynh

Thinh Ong

Marc Choisy

transform_data function

<dl><dt>t</dt>
<dd>the time vector.</dd>
<dt>spos</dt>
<dd>the seropositive vector.</dd>
<dt>heterogeneity_col</dt>
<dd>new name for the time vector (default to "t")</dd></dl>

Arguments

Generate a dataframe with `t`, `pos` and `tot` columns from
`t` and `seropositive` vectors. — transform_data

<dl>

<dt>t</dt>
<dd>the time vector.</dd>


<dt>spos</dt>
<dd>the seropositive vector.</dd>


<dt>heterogeneity_col</dt>
<dd>new name for the time vector (default to "t")</dd>

</dl>

transform_data: Generate a dataframe with `t`, `pos` and `tot` columns from `t` and `seropositive` vectors.

Description

Usage

Value

Arguments

Examples