evalHillModel

Evaluate a Hill dose response model

Evaluate, fit, and analyze Hill dose response models (Goutelle et
al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred
to as four-parameter log-logistic models. Includes tools to invert Hill
models, select models based on the Akaike information criterion
(Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information
criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and
construct bootstrapped confidence intervals both
on the Hill model parameters and values derived from the Hill model
parameters.

Nathaniel Twarog

basicdrm

Fit Hill Dose Response Models

Anang Shelat

evalHillModel function

<dl><dt>conc</dt>
<dd>A vector of concentrations (including 0 or Inf)</dd>
<dt>hpar</dt>
<dd>A four parameter vector specifying the Hill model. The values of
the parameter vector are, in order, the dose of median effect (also often
referred to as the EC50), the Hill slope, the minimal effect (observed when
no drug or dose is present), and the maximal effect (theoretically observed
when the drug or dose is infinite).</dd></dl>

Arguments

Evaluate a Hill dose response model — evalHillModel

<dl>

<dt>conc</dt>
<dd>A vector of concentrations (including 0 or Inf)</dd>


<dt>hpar</dt>
<dd>A four parameter vector specifying the Hill model. The values of
the parameter vector are, in order, the dose of median effect (also often
referred to as the EC50), the Hill slope, the minimal effect (observed when
no drug or dose is present), and the maximal effect (theoretically observed
when the drug or dose is infinite).</dd>

</dl>

evalHillModel: Evaluate a Hill dose response model

Description

Usage

Value

Arguments

Examples