boot.misclassMlogit

Obtain bootstrapped standard errors.

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

boot.misclassMlogit function

<dl><dt>ret</dt>
<dd>a fitted object of class inheriting from 'misclassMlogit'.</dd>
<dt>Y</dt>
<dd>a matrix of 0s and 1s, indicating the target class. This is the dependent variable.</dd>
<dt>X</dt>
<dd>a matrix containing the independent variables.</dd>
<dt>Pmodel</dt>
<dd>a fitted model (e.g. of class 'GLM' or 'mlogit') to implicitly produce variations
of the predicted true values probabilities. (Usually conditional on the observed
misclassified values and additional covariates.)</dd>
<dt>PX</dt>
<dd>covariates matrix suitable for predicting probabilities from <code>Pmodel</code>,
usually including the mismeasured covariate.</dd>
<dt>boot.fraction</dt>
<dd>fraction of sample to be used for estimating the bootstrapped standard
errors, for speedup.</dd>
<dt>repetitions</dt>
<dd>number of bootstrap samples to be drown.</dd></dl>

Arguments

Compute Bootstrapped Standard Errors for misclassMlogit Fits — boot.misclassMlogit

<dl>

<dt>ret</dt>
<dd>a fitted object of class inheriting from 'misclassMlogit'.</dd>


<dt>Y</dt>
<dd>a matrix of 0s and 1s, indicating the target class. This is the dependent variable.</dd>


<dt>X</dt>
<dd>a matrix containing the independent variables.</dd>


<dt>Pmodel</dt>
<dd>a fitted model (e.g. of class 'GLM' or 'mlogit') to implicitly produce variations
of the predicted true values probabilities. (Usually conditional on the observed
misclassified values and additional covariates.)</dd>


<dt>PX</dt>
<dd>covariates matrix suitable for predicting probabilities from <code>Pmodel</code>,
usually including the mismeasured covariate.</dd>


<dt>boot.fraction</dt>
<dd>fraction of sample to be used for estimating the bootstrapped standard
errors, for speedup.</dd>


<dt>repetitions</dt>
<dd>number of bootstrap samples to be drown.</dd>

</dl>

boot.misclassMlogit: Compute Bootstrapped Standard Errors for `misclassMlogit` Fits

Description

Usage

Arguments

See Also