predict.MarginalFit

Predict values on the dose-response curve

Response surface methods for drug synergy analysis. Available
methods include generalized and classical Loewe formulations as well as Highest
Single Agent methodology. Response surfaces can be plotted in an interactive
3-D plot and formal statistical tests for presence of synergistic effects are
available. Implemented methods and tests are described in the article
"BIGL: Biochemically Intuitive Generalized Loewe null model for prediction
of the expected combined effect compatible with partial agonism and antagonism"
by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas,
Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017)
<doi:10.1038/s41598-017-18068-5>.

Kathy Mutambanengwe

BIGL

Biochemically Intuitive Generalized Loewe Model

Heather Turner

Annelies Tourny

Olivier Thas

Maxim Nazarov

Rytis Bagdziunas

Stijn Hawinkel

predict.MarginalFit function

<dl><dt>object</dt>
<dd>Output of <code>fitMarginals</code> function</dd>
<dt>newdata</dt>
<dd>An optional data frame in which to look for <code>d1</code> and
<code>d2</code> variables with which to predict. If omitted, the fitted values
are used. Doses that are passed to this function must correspond to
marginal data, i.e. at least one of the doses must be zero.</dd>
<dt>...</dt>
<dd>Further arguments</dd></dl>

Arguments

Predict values on the dose-response curve — predict.MarginalFit

<dl>

<dt>object</dt>
<dd>Output of <code>fitMarginals</code> function</dd>


<dt>newdata</dt>
<dd>An optional data frame in which to look for <code>d1</code> and
<code>d2</code> variables with which to predict. If omitted, the fitted values
are used. Doses that are passed to this function must correspond to
marginal data, i.e. at least one of the doses must be zero.</dd>


<dt>...</dt>
<dd>Further arguments</dd>

</dl>

predict.MarginalFit: Predict values on the dose-response curve

Description

Usage

Arguments