This function prepares data to fit a hierarchical Bayesian Aldrich-McKelvey (HBAM) model. It can be run ahead of fitting the models, or it can be run implicitly as part of a single function call to fit the models using hbam() or fbam(). It applies a set of inclusion criteria, performs any necessary data transformation, and returns a list of data suited for sampling in rstan. The data provided to prep_data() can be centered, but they do not have to be: The function will detect un-centered data and attempt to center these automatically, assuming that the highest and lowest observed values in the data mark the extremes of the scale.
Usage
prep_data(
self = NULL,
stimuli,
prefs = NULL,
allow_miss = 2,
req_valid = NA,
req_unique = 2,
B = NULL,
group_id = NULL,
quiet = FALSE
)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, respondent positions will not be estimated.
- stimuli
An N × J matrix of numerical stimulus placements, where J is the number of stimuli. Any missing data must be coded as NA.
- prefs
An N × J matrix of numerical stimulus ratings or preference scores. These data are only required by the
"HBAM_R_MINI"model and will be ignored when fitting other models.- allow_miss
Integer specifying how many missing stimulus positions to be accepted for an individual still to be included in the analysis. 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_validtakes precedence.- 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.- B
Integer specifying the upper bound of the survey scale after centering. If not supplied, this information will be inferred from the data.
- 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_idwill be dropped whengroup_idis supplied. These data are only required by models with"MULTI"in their name and will be ignored when fitting other models.- quiet
Logical: Should information about the data be printed to the console? Defaults to
TRUE.
Value
A list of data to be used by hbam() or fbam(). The returned list includes the logical vector keep, which identifies the rows in the original data that have been kept for further analysis. The stimuli data are stored in a vector as a long-form sparse matrix. If the stimuli data include column-names, these will be preserved for later use.
Examples
# Loading and re-coding ANES 1980 data:
data(LC1980)
LC1980[LC1980 == 0 | LC1980 == 8 | LC1980 == 9] <- NA
self <- LC1980[, 1]
stimuli <- LC1980[, -1]
# Prepare data for model fitting, using defaults:
dat <- prep_data(self, stimuli)
#> Summary of prepared data (values for supplied data in paretheses)
#> - Number of respondents: 881 (888)
#> - Number of stimuli: 6 (6)
#> - Number of stimuli obs.: 4937 (4973)
#> - Range of observations: [-3, 3] ([1, 7])
# Prepare data for model fitting, using using alternative settings:
dat2 <- prep_data(self, stimuli, allow_miss = 0, req_unique = 3)
#> Summary of prepared data (values for supplied data in paretheses)
#> - Number of respondents: 603 (888)
#> - Number of stimuli: 6 (6)
#> - Number of stimuli obs.: 3618 (4973)
#> - Range of observations: [-3, 3] ([1, 7])
# Obtain the data that are included in the analysis:
self2 <- self[dat2$keep]
stimuli2 <- stimuli[dat2$keep, ]