`R/utils.R`

`logit_probs.Rd`

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.

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

- 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.

```
library(logitr)
# Estimate a preference space model
mnl_pref <- logitr(
data = yogurt,
outcome = "choice",
obsID = "obsID",
pars = c("price", "feat", "brand")
)
#> Running model...
#> Done!
# 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
)
```