fit_bounded_logistic

Curve is of form f(y) = pmin + (pmax-pmin) * logistic( beta0 + beta1*x )

internal

Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.

Luke Miratrix

PUMP

Power Under Multiplicity Project

Kristen Hunter

Zarni Htet

Kristin Porter

MDRC 

Institute of Education Sciences 

fit_bounded_logistic function

<dl><dt>x</dt>
<dd>The vector of covariate values of the logistics</dd>
<dt>y</dt>
<dd>The proportion of 1s for a given value of x. Same length as x.</dd>
<dt>wt</dt>
<dd>The weight to place on a given x-y pair. Same length as x, or
scalar.</dd></dl>

Arguments

Fit a bounded logistic curve — fit_bounded_logistic

<dl>

<dt>x</dt>
<dd>The vector of covariate values of the logistics</dd>


<dt>y</dt>
<dd>The proportion of 1s for a given value of x. Same length as x.</dd>


<dt>wt</dt>
<dd>The weight to place on a given x-y pair. Same length as x, or
scalar.</dd>

</dl>

fit_bounded_logistic: Fit a bounded logistic curve

Description

Usage

Value

Arguments

Details