mfx.misclassGLM

Estimates models that extend the standard GLM to take
misclassification into account. The models require side information from a secondary data set
on the misclassification process, i.e. some sort of misclassification
probabilities conditional on some common covariates.
A detailed description of the algorithm can be found in
Dlugosz, Mammen and Wilke (2015) <https://www.zew.de/publikationen/generalised-partially-linear-regression-with-misclassified-data-and-an-application-to-labour-market-transitions>.

Stephan Dlugosz

misclassGLM

Computation of Generalized Linear Models with Misclassified
Covariates Using Side Information

mfx.misclassGLM function

<dl><dt>w</dt>
<dd>a fitted object of class inheriting from 'misclassGLM'.</dd>
<dt>x.mean</dt>
<dd>logical, if true computes marginal effects at mean, otherwise average marginal
effects.</dd>
<dt>rev.dum</dt>
<dd>logical, if true, computes differential effects for switch from 0 to 1.</dd>
<dt>digits</dt>
<dd>number of digits to be presented in output.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other functions.</dd></dl>

Arguments

Compute Marginal Effects for misclassGLM Fits — mfx.misclassGLM

<dl>

<dt>w</dt>
<dd>a fitted object of class inheriting from 'misclassGLM'.</dd>


<dt>x.mean</dt>
<dd>logical, if true computes marginal effects at mean, otherwise average marginal
effects.</dd>


<dt>rev.dum</dt>
<dd>logical, if true, computes differential effects for switch from 0 to 1.</dd>


<dt>digits</dt>
<dd>number of digits to be presented in output.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other functions.</dd>

</dl>

mfx.misclassGLM: Compute Marginal Effects for `misclassGLM` Fits

Description

Usage

Arguments

See Also