yogurt

<p>Data from Jain et al. (1994) containing 2,412 choice observations from a
series of yogurt purchases by a panel of 100 households in Springfield,
Missouri, over a roughly two-year period. The data were collected by
optical scanners and contain information about the price, brand, and a
"feature" variable, which identifies whether a newspaper advertisement was
shown to the customer. There are four brands of yogurt: Yoplait, Dannon,
Weight Watchers, and Hiland, with market shares of 34%, 40%, 23% and 3%,
respectively.</p>

datasets

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

yogurt function


<table class="table">
<tr><td>Variable</td><td>Description</td></tr>
<tr><td><code>id</code></td><td>individual identifiers</td></tr>
<tr><td><code>obsID</code></td><td>identifier for unique choice observation</td></tr>
<tr><td><code>alt</code></td><td>alternative in each choice observation</td></tr>
<tr><td><code>choice</code></td><td>dummy code for choice (1 or 0)</td></tr>
<tr><td><code>price</code></td><td>price of yogurt</td></tr>
<tr><td><code>feat</code></td><td>dummy for whether a newspaper advertisement was shown to the customer (<code>1</code> or <code>0</code>)</td></tr>
<tr><td><code>brand</code></td><td>yogurt brand: <code>"yoplait"</code>, <code>"dannon"</code>, <code>"hiland"</code>, or <code>"weight"</code> (for weight watcher)</td></tr>
</table><p></p><p></p>

Format

Choice observations of yogurt purchases by 100 households — yogurt


<table class='table'>
<tr><td>Variable</td><td>Description</td></tr>
<tr><td><code>id</code></td><td>individual identifiers</td></tr>
<tr><td><code>obsID</code></td><td>identifier for unique choice observation</td></tr>
<tr><td><code>alt</code></td><td>alternative in each choice observation</td></tr>
<tr><td><code>choice</code></td><td>dummy code for choice (1 or 0)</td></tr>
<tr><td><code>price</code></td><td>price of yogurt</td></tr>
<tr><td><code>feat</code></td><td>dummy for whether a newspaper advertisement was shown to the customer (<code>1</code> or <code>0</code>)</td></tr>
<tr><td><code>brand</code></td><td>yogurt brand: <code>"yoplait"</code>, <code>"dannon"</code>, <code>"hiland"</code>, or <code>"weight"</code> (for weight watcher)</td></tr>
</table>



Variable	Description
`id`	individual identifiers
`obsID`	identifier for unique choice observation
`alt`	alternative in each choice observation
`choice`	dummy code for choice (1 or 0)
`price`	price of yogurt
`feat`	dummy for whether a newspaper advertisement was shown to the customer (`1` or `0`)
`brand`	yogurt brand: `"yoplait"`, `"dannon"`, `"hiland"`, or `"weight"` (for weight watcher)

yogurt: Choice observations of yogurt purchases by 100 households

Description

Usage

Arguments

Format

References

Examples