Fit an FBAM model using optimization

Fit a simplified Bayesian Aldrich-McKelvey model with fixed hyperparameters using optimization via rstan. Users may replace the default priors by supplying their own values for the hyperparameters.

Usage

fbam(
  self = NULL,
  stimuli = NULL,
  model = "FBAM",
  allow_miss = 2,
  req_valid = NA,
  req_unique = 2,
  group_id = NULL,
  data = NULL,
  seed = sample.int(.Machine$integer.max, 1),
  sigma_alpha = NULL,
  sigma_beta = 0.3,
  sigma_mu_alpha = NULL,
  sigma_mu_beta = 0.2,
  ...
)

Arguments

self: An optional numerical vector of N ideological self-placements. Any missing data must be coded as NA. If this argument is not supplied (either here or in a previous call to prep_data()), respondent positions will not be estimated. If the data have been prepared in advance via the prep_data() function, the argument supplied here will be ignored.
stimuli: An N × J matrix of numerical stimulus placements, where J is the number of stimuli. Any missing data must be coded as NA. This argument will not be used if the data have been prepared in advance via the prep_data() function.
model: Character: Name of the model to be used. Defaults to FBAM. The available options are the three models with "FBAM" in their name. See the documentation for the hbam() function for descriptions of the models.
allow_miss: Integer specifying how many missing stimulus positions to be accepted for an individual still to be included in the analysis. This argument will not be used if the data have been prepared in advance via the prep_data() function. Defaults to 2.
req_valid: Integer specifying how many valid observations to require for a respondent to be included in the analysis. The default is req_valid = J - allow_miss, but if specified, req_valid takes precedence. This argument will not be used if the data have been prepared in advance via the prep_data() function.
req_unique: Integer specifying how may unique positions on the ideological scale each respondent is required to have used when placing the stimuli in order to be included in the analysis. The default is req_unique = 2. This argument will not be used if the data have been prepared in advance via the prep_data() function.
group_id: Vector of length N identifying which group each respondent belongs to. The format can be factor, character, integer, or numeric. Respondents with NAs on group_id will be dropped when group_id is supplied. These data are only required by models with "MULTI" in their name and will be ignored when fitting other models.
data: List of data that have been prepared in advance via the prep_data() function. Not required if the arguments self and stimuli are provided.
seed: A positive integer specifying an optional seed for reproducibility. If this argument is not supplied, a random seed will be generated and the function will produce slightly different results on each run.
sigma_alpha: A positive numeric value specifying the standard deviation of the prior on the shift parameters in the FBAM model, or the standard deviation of the parameters' deviation from the group-means in FBAM_MULTI models. (This argument will be ignored by HBAM models.) Defaults to B / 5, where B measures the length of the survey scale as the number of possible placements on one side of the center.
sigma_beta: A positive numeric value specifying the standard deviation of the prior on the logged stretch parameters in the FBAM model, or the standard deviation of the logged parameters' deviation from the group-means in FBAM_MULTI models. (This argument will be ignored by HBAM models.) Defaults to .3.
sigma_mu_alpha: A positive numeric value specifying the standard deviation of the prior on the group-means of the shift parameters in MULTI-type models. Defaults to B / 10.
sigma_mu_beta: A positive numeric value specifying the standard deviation of the prior on the group-means of the logged stretch parameters in MULTI-type models. Defaults to .2.
...: Arguments passed to rstan::optimizing().

Value

A list produced by rstan::optimizing().

Examples

# \donttest{
# Loading ANES 2012 data:
data(LC2012)

self <- LC2012[, 2]
stimuli <- LC2012[, -c(1:2)]

# Fitting the FBAM model:
fit_fbam <- fbam(self, stimuli)

# Obtaining point estimates for the latent stimulus positions:
theta_est <- get_est(fit_fbam, par = "theta")
# }