dummyCode

This function is depreciated. Use fastDummies::dummy_cols() instead.

internal

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.

John Helveston

logitr

Logit Models w/Preference & WTP Space Utility Parameterizations

Connor Forsythe

dummyCode function

<dl><dt>df</dt>
<dd>A data frame.</dd>
<dt>vars</dt>
<dd>The variables in the data frame for which you want to
create new dummy coded variables.</dd></dl>

Arguments

Add dummy-coded variables to data frame. — dummyCode

<dl>

<dt>df</dt>
<dd>A data frame.</dd>


<dt>vars</dt>
<dd>The variables in the data frame for which you want to
create new dummy coded variables.</dd>

</dl>

dummyCode: Add dummy-coded variables to data frame.

Description

Usage

Value

Arguments