compute_ci.penalized_spline_model

Compute confidence interval for penalized_spline_model

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

compute_ci.penalized_spline_model function

<dl><dt>x</dt>
<dd>- serosv models</dd>
<dt>ci</dt>
<dd>- confidence interval</dd>
<dt>...</dt>
<dd>- arbitrary arguments</dd></dl>

Arguments

Compute confidence interval for penalized_spline_model — compute_ci.penalized_spline_model

<dl>

<dt>x</dt>
<dd>- serosv models</dd>


<dt>ci</dt>
<dd>- confidence interval</dd>


<dt>...</dt>
<dd>- arbitrary arguments</dd>

</dl>

compute_ci.penalized_spline_model: Compute confidence interval for penalized_spline_model

Description

Usage

Value

Arguments