fee_negbin

Computes the first-exposure effect (FEE) for a model estimated using the
one-inflated zero-truncated negative binomial distribution. The FEE represents
the change in expected counts associated with the first exposure to treatment,
relative to the counterfactual zero-truncated negative binomial model. This
function is used internally by <code>fee</code> at given covariate values.

internal

Estimates the first-exposure effect (FEE) using a
one-inflated positive Poisson model, or a one-inflated zero-truncated
negative binomial model. In addition, estimates the marginal FEE, and
standard errors for the FEE and marginal FEE.

Ryan T. Godwin

Estimate the First-Exposure Effect (FEE) using Count Data Models

fee_negbin function

<dl><dt>b</dt>
<dd>A numeric vector of estimated coefficients for the rate.</dd>
<dt>g</dt>
<dd>A numeric vector of estimated coefficients for inflation.</dd>
<dt>a</dt>
<dd>A dispersion parameter from the negative binomial model.</dd>
<dt>X</dt>
<dd>A numeric matrix of covariates for the rate.</dd>
<dt>Z</dt>
<dd>A numeric matrix of covariates for inflation.</dd></dl>

Arguments

First-Exposure Effect for One-Inflated Zero-Truncated Negative Binomial Model — fee_negbin

<dl>

<dt>b</dt>
<dd>A numeric vector of estimated coefficients for the rate.</dd>


<dt>g</dt>
<dd>A numeric vector of estimated coefficients for inflation.</dd>


<dt>a</dt>
<dd>A dispersion parameter from the negative binomial model.</dd>


<dt>X</dt>
<dd>A numeric matrix of covariates for the rate.</dd>


<dt>Z</dt>
<dd>A numeric matrix of covariates for inflation.</dd>

</dl>

fee_negbin: First-Exposure Effect for One-Inflated Zero-Truncated Negative Binomial Model

Description

Usage

Value

Arguments

See Also