# Data Formatting and Encoding

# Basic required format

The {logitr} package requires that data be structured in a `data.frame` and arranged in a “long” format [@Wickham2014] where each row contains data on a single alternative from a choice observation. The choice observations do not have to be symmetric, meaning they can have a “ragged” structure where different choice observations have different numbers of alternatives. The data must also include variables for each of the following:

- Outcome: A dummy-coded variable that identifies which alternative was chosen (`1` is chosen, `0` is not chosen). Only one alternative should have a `1` per choice observation.
- Observation ID: A sequence of repeated numbers that identifies each unique choice observation. For example, if the first three choice observations had 2 alternatives each, then the first 6 rows of the `obsID` variable would be `1, 1, 2, 2, 3, 3`.
- Covariates: Other variables that will be used as model covariates.

The {logitr} package contains several example data sets that illustrate this data structure. For example, the `yogurt` contains observations of yogurt purchases by a panel of 100 households [@Jain1994]. Choice is identified by the `choice` column, the observation ID is identified by the `obsID` column, and the columns `price`, `feat`, and `brand` can be used as model covariates:

    library("logitr")

    head(yogurt)
    #> # A tibble: 6 × 7
    #>      id obsID   alt choice price  feat brand
    #>   <dbl> <int> <int>  <dbl> <dbl> <dbl> <chr>
    #> 1     1     1     1      0  8.1      0 dannon
    #> 2     1     1     2      0  6.10     0 hiland
    #> 3     1     1     3      1  7.90     0 weight
    #> 4     1     1     4      0 10.8      0 yoplait
    #> 5     1     2     1      1  9.80     0 dannon
    #> 6     1     2     2      0  6.40     0 hiland

This data set also includes an `alt` variable that determines the alternatives included in the choice set of each observation and an `id` variable that determines the individual as the data have a panel structure containing multiple choice observations from each individual.

# Continuous versus discrete variables

Variables are modeled as either continuous or discrete based on their data type. Numeric variables are by default estimated with a single “slope” coefficient. For example, consider a data frame that contains a `price` variable with the levels $10, $15, and $20. Adding `price` to the `pars` argument in the main `logitr()` function would result in a single `price` coefficient for the “slope” of the change in price.

In contrast, categorical variables (i.e. `character` or `factor` type variables) are by default estimated with a coefficient for all but the first level, which serves as the reference level. The default reference level is determined alphabetically, but it can also be set by modifying the factor levels for that variable. For example, the default reference level for the `brand` variable is `"dannon"` as it is alphabetically first. To set `"weight"` as the reference level, the factor levels can be modified using the `factor()` function:

    yogurt2 <- yogurt

    brands <- c("weight", "hiland", "yoplait", "dannon")
    yogurt2$brand <- factor(yogurt2$brand, levels = brands)

# Creating dummy coded variables

If you wish to make dummy-coded variables yourself to use them in a model, I recommend using the `dummy_cols()` function from the {fastDummies} package. For example, in the code below, I create dummy-coded columns for the `brand` variable and then use those variables as covariates in a model:

    yogurt2 <- fastDummies::dummy_cols(yogurt2, "brand")

The `yogurt2` data frame now has new dummy-coded columns for brand:

    head(yogurt2)
    #> # A tibble: 6 × 11
    #>      id obsID   alt choice price  feat brand   brand_weight brand_hiland
    #>   <dbl> <int> <int>  <dbl> <dbl> <dbl> <fct>          <int>        <int>
    #> 1     1     1     1      0  8.1      0 dannon             0            0
    #> 2     1     1     2      0  6.10     0 hiland             0            1
    #> 3     1     1     3      1  7.90     0 weight             1            0
    #> 4     1     1     4      0 10.8      0 yoplait            0            0
    #> 5     1     2     1      1  9.80     0 dannon             0            0
    #> 6     1     2     2      0  6.40     0 hiland             0            1
    #> # ℹ 2 more variables: brand_yoplait <int>, brand_dannon <int>

Now I can use those columns as covariates:

    mnl_pref_dummies <- logitr(
      data    = yogurt2,
      outcome = 'choice',
      obsID   = 'obsID',
      pars    = c(
        'price', 'feat', 'brand_yoplait', 'brand_dannon', 'brand_weight'
      )
    )

    summary(mnl_pref_dummies)
    #> =================================================
    #>
    #> Model estimated on: Mon Jul 06 06:51:06 2026
    #>
    #> Using logitr version: 1.2.0
    #>
    #> Call:
    #> logitr(data = yogurt2, outcome = "choice", obsID = "obsID", pars = c("price",
    #>     "feat", "brand_yoplait", "brand_dannon", "brand_weight"))
    #>
    #> Frequencies of alternatives:
    #>        1        2        3        4
    #> 0.402156 0.029436 0.229270 0.339138
    #>
    #> Exit Status: 3, Optimization stopped because ftol_rel or ftol_abs was reached.
    #>
    #> Model Type:    Multinomial Logit
    #> Model Space:          Preference
    #> Model Run:                1 of 1
    #> Iterations:                   21
    #> Elapsed Time:        0h:0m:0.01s
    #> Algorithm:        NLOPT_LD_LBFGS
    #> Weights Used?:             FALSE
    #> Robust?                    FALSE
    #>
    #> Model Coefficients:
    #>                Estimate Std. Error  z-value  Pr(>|z|)
    #> price         -0.366584   0.024366 -15.0449 < 2.2e-16 ***
    #> feat           0.491433   0.120063   4.0931 4.256e-05 ***
    #> brand_yoplait  4.450171   0.187118  23.7827 < 2.2e-16 ***
    #> brand_dannon   3.715600   0.145419  25.5510 < 2.2e-16 ***
    #> brand_weight   3.074416   0.145384  21.1469 < 2.2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Log-Likelihood:         -2656.8878779
    #> Null Log-Likelihood:    -3343.7419990
    #> AIC:                     5323.7757559
    #> BIC:                     5352.7168000
    #> McFadden R2:                0.2054148
    #> Adj McFadden R2:            0.2039195
    #> Number of Observations:  2412.0000000

# Validating data before estimation

Before estimating a model, it is often helpful to validate that your data is properly formatted. The `validate_data()` function checks for common formatting errors and provides detailed diagnostic information. This can save time by catching errors before you attempt to estimate a model.

## Basic validation

At a minimum, you should validate the `outcome` and `obsID` columns:

    validation <- validate_data(
      data = yogurt,
      outcome = "choice",
      obsID = "obsID"
    )

    validation
    #> === LOGITR DATA VALIDATION ===
    #>
    #> Data Overview:
    #>   Rows: 9648
    #>   Columns: 7
    #>   Outcome variable: choice
    #>   Observation ID: obsID
    #>
    #> Data Structure:
    #>   Total observations: 2412
    #>   Total alternatives: 9648
    #>   Valid choices: 2412
    #>   Alternatives per observation:
    #>      4 alternatives: 2412 observations
    #>
    #> === VALIDATION RESULTS ===
    #> ✓ Data validation PASSED - no issues found!
    #> ✓ Data appears ready for use with logitr()!

The function returns a validation object that indicates whether the data is valid and provides summary information about the data structure.

## Validation with parameters

You can also validate specific parameters to check for missing values or other issues:

    validation <- validate_data(
      data = yogurt,
      outcome = "choice",
      obsID = "obsID",
      pars = c("price", "feat", "brand")
    )

    validation
    #> === LOGITR DATA VALIDATION ===
    #>
    #> Data Overview:
    #>   Rows: 9648
    #>   Columns: 7
    #>   Outcome variable: choice
    #>   Observation ID: obsID
    #>   Parameters: price, feat, brand
    #>
    #> Data Structure:
    #>   Total observations: 2412
    #>   Total alternatives: 9648
    #>   Valid choices: 2412
    #>   Alternatives per observation:
    #>      4 alternatives: 2412 observations
    #>
    #> Parameter Information:
    #>    price ( numeric ): range [ 0.3 , 19.3 ]
    #>    feat ( numeric ): range [ 0 , 1 ]
    #>    brand ( character ):  4 levels - dannon, hiland, weight, yoplait
    #>
    #> === VALIDATION RESULTS ===
    #> ✓ Data validation PASSED - no issues found!
    #> ✓ Data appears ready for use with logitr()!

## Panel data validation

For panel data, you can validate the panel structure:

    validation <- validate_data(
      data = yogurt,
      outcome = "choice",
      obsID = "obsID",
      pars = c("price", "feat", "brand"),
      panelID = "id"
    )

    validation
    #> === LOGITR DATA VALIDATION ===
    #>
    #> Data Overview:
    #>   Rows: 9648
    #>   Columns: 7
    #>   Outcome variable: choice
    #>   Observation ID: obsID
    #>   Parameters: price, feat, brand
    #>   Panel ID: id
    #>
    #> Data Structure:
    #>   Total observations: 2412
    #>   Total alternatives: 9648
    #>   Valid choices: 2412
    #>   Alternatives per observation:
    #>      4 alternatives: 2412 observations
    #>   Panel structure:
    #>     Individuals: 100
    #>     Observations per individual: Min = 4 , Max = 185 , Mean = 24.1
    #>
    #> Parameter Information:
    #>    price ( numeric ): range [ 0.3 , 19.3 ]
    #>    feat ( numeric ): range [ 0 , 1 ]
    #>    brand ( character ):  4 levels - dannon, hiland, weight, yoplait
    #>
    #> === VALIDATION RESULTS ===
    #> ✓ Data validation PASSED - no issues found!
    #> ✓ Data appears ready for use with logitr()!

## Common errors detected

The `validate_data()` function checks for several common formatting errors:

1. Multiple choices per observation: Each `obsID` should have exactly one choice (outcome = 1)
2. No choice in observation: Each `obsID` must have at least one choice
3. Non-contiguous observation blocks: All rows with the same `obsID` must be grouped together
4. Invalid outcome values: The outcome variable must only contain 0 and 1 (or TRUE and FALSE)
5. Missing values: Checks for missing values in required columns

Here’s an example of detecting an error:

    # Create problematic data with multiple choices in one observation
    bad_data <- yogurt
    bad_data$choice[1:2] <- 1

    validation <- validate_data(
      data = bad_data,
      outcome = "choice",
      obsID = "obsID"
    )

    validation
    #> === LOGITR DATA VALIDATION ===
    #>
    #> Data Overview:
    #>   Rows: 9648
    #>   Columns: 7
    #>   Outcome variable: choice
    #>   Observation ID: obsID
    #>
    #> Data Structure:
    #>   Total observations: 2412
    #>   Total alternatives: 9648
    #>   Valid choices: 2411
    #>   Alternatives per observation:
    #>      4 alternatives: 2412 observations
    #>
    #> === VALIDATION RESULTS ===
    #> ✗ ERRORS found:
    #>    1 . Multiple choices (>1) found in obsID(s): 1
    #>
    #>   Detailed locations for multiple choices:
    #>     ObsID 1 - rows: 1, 2, 3, 4
    #>
    #>  Please fix the errors above before using with logitr().