predict.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

predict.misclassGLM function

<dl><dt>object</dt>
<dd>a fitted object of class inheriting from 'misclassGLM'.</dd>
<dt>X</dt>
<dd>matrix of fixed covariates</dd>
<dt>P</dt>
<dd>a-posteriori probabilities for the true values of the misclassified variable.
If provided, the conditional expectation on X,P is computed, otherwise a set of marginal
predictions is provided, one for each alternative.</dd>
<dt>type</dt>
<dd>the type of prediction required. The default is on the scale of the linear predictors;
 the alternative "response" is on the scale of the response variable.
 Thus for a default binomial model the default predictions are of log-odds
 (probabilities on logit scale) and type = "response" gives the predicted probabilities.
The value of this argument can be abbreviated.</dd>
<dt>na.action</dt>
<dd>function determining what should be done with missing values in <code>newdata</code>.
The default is to predict NA.</dd>
<dt>...</dt>
<dd>additional arguments (not used at the moment)</dd></dl>

Arguments

Predict Method for misclassGLM Fits — predict.misclassGLM

<dl>

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


<dt>X</dt>
<dd>matrix of fixed covariates</dd>


<dt>P</dt>
<dd>a-posteriori probabilities for the true values of the misclassified variable.
If provided, the conditional expectation on X,P is computed, otherwise a set of marginal
predictions is provided, one for each alternative.</dd>


<dt>type</dt>
<dd>the type of prediction required. The default is on the scale of the linear predictors;
 the alternative "response" is on the scale of the response variable.
 Thus for a default binomial model the default predictions are of log-odds
 (probabilities on logit scale) and type = "response" gives the predicted probabilities.
The value of this argument can be abbreviated.</dd>


<dt>na.action</dt>
<dd>function determining what should be done with missing values in <code>newdata</code>.
The default is to predict NA.</dd>


<dt>...</dt>
<dd>additional arguments (not used at the moment)</dd>

</dl>

predict.misclassGLM: Predict Method for `misclassGLM` Fits

Description

Usage

Arguments

See Also