simulate_GLM_dataset

simulates a data set with
- one continuous variable X drawn from a Gaussian distribution,
- a binary or trinary variable M with misclassification (M2)
- a dependent variable either with added Gaussian noise or drawn from a logit distribution

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

simulate_GLM_dataset function

<dl><dt>n</dt>
<dd>number observations</dd>
<dt>const</dt>
<dd>constant</dd>
<dt>alpha</dt>
<dd>parameter for X</dd>
<dt>beta</dt>
<dd>parameter for M(1)</dd>
<dt>beta2</dt>
<dd>parameter for M2, if NULL, M is a binary covariate, otherwise a three-valued
categorical</dd>
<dt>logit</dt>
<dd>logical, if true logit regression, otherwise Gaussian regression</dd></dl>

Arguments

Simulate a Data Set to Use With misclassGLM — simulate_GLM_dataset

<dl>

<dt>n</dt>
<dd>number observations</dd>


<dt>const</dt>
<dd>constant</dd>


<dt>alpha</dt>
<dd>parameter for X</dd>


<dt>beta</dt>
<dd>parameter for M(1)</dd>


<dt>beta2</dt>
<dd>parameter for M2, if NULL, M is a binary covariate, otherwise a three-valued
categorical</dd>


<dt>logit</dt>
<dd>logical, if true logit regression, otherwise Gaussian regression</dd>

</dl>

simulate_GLM_dataset: Simulate a Data Set to Use With `misclassGLM`

Description

Usage

Arguments

Details

See Also