logitr (version 1.1.2)

logit_probs: Compute logit fraction for sets of alternatives given coefficient draws

Description

Returns a data frame of the predicted probabilities (with a confidence interval) for a data frame of alternatives given coefficient draws. WARNING: Most of the time you probably want to use predict() instead of this function. Where logit_probs() is useful is if you estimate a model with an interaction parameter to see differences between groups. In those cases, you can obtain draws of the estimated parameters and then use the draws to predict probabilities for each group after summing together the appropriate columns of the draws for each group. Also note that this function is only useful for multinomial logit models and is not appropriate for mixed logit models.

Usage

logit_probs(object, coef_draws, newdata, obsID = NULL, level = 0.95)

Arguments

object

is an object of class logitr (a model estimated using the 'logitr()` function).

coef_draws

A data frame of coefficients draws.

newdata

A data frame of sets of alternatives for which to compute logit probabilities. Each row is an alternative.

obsID

The name of the column in newdata that identifies each set of alternatives. Defaults to NULL, in which case it assumes the newdata are all one choice scenario.

level

The sensitivity of the computed confidence interval (CI). Defaults to level = 0.95, reflecting a 95% CI.

Examples

library(logitr)

# Estimate a preference space model
mnl_pref <- logitr(
  data    = yogurt,
  outcome = "choice",
  obsID   = "obsID",
  pars    = c("price", "feat", "brand")
)

# Create a set of alternatives for which to simulate probabilities
# (Columns are attributes, rows are alternatives)
data <- data.frame(
  altID       = c(1, 2, 3, 4),
  obsID       = c(1, 1, 1, 1),
  price       = c(8, 6, 7, 10),
  feat    = c(0, 1, 0, 0),
  brand   = c('dannon', 'hiland', 'weight', 'yoplait')
)

# Obtain 10,000 draws of parameters from model
coefs <- coef(mnl_pref)
covariance <- vcov(mnl_pref)
coef_draws <- as.data.frame(MASS::mvrnorm(10^4, coefs, covariance))

# Compute the probabilities
sim <- logit_probs(
  mnl_pref,
  coef_draws = coef_draws,
  newdata = data,
  obsID = 'obsID',
  level = 0.95
)