The object bam.france is a result of applying the BAM() function to a dataset (bamdata) with specific parameters
to analyze French political data. This object contains the Bayesian Aldrich-McKelvey scaling results.
Format
A list of class BAM containing the following components:
- polarity
The polarity of the analysis, set to 2 in this case. This indicates the polarity constraint applied during the scaling.
- n.adapt
The number of iterations used for adaptation, which was 2500 in this case.
- n.sample
The number of MCMC samples collected, which was 5000 in this case.
- zhat
A logical value indicating whether the ideal points should be adjusted for mean-zero scaling (
zhat=TRUE).- ab
A logical value indicating whether the response ideal points are used (
ab=TRUE).- resp.idealpts
A logical value indicating whether to estimate respondent ideal points (
resp.idealpts=TRUE).- data
The original data used in the analysis.
- idealpoints
The estimated ideal points from the BAM analysis.
- posteriors
Posterior distributions of the ideal points and other parameters.
- convergence
Convergence diagnostics for the MCMC chains.
- other_components
Additional components that store the results and diagnostics of the BAM analysis.
Source
The BAM model was applied to a dataset bamdata with specific settings to generate bam.france.
Details
The bam.france object was created using the BAM() function with the following parameters:
bam.france <- BAM(bamdata, polarity=2, n.adapt=2500, n.sample=5000, zhat=TRUE, ab=TRUE, resp.idealpts=TRUE)
This analysis uses a Bayesian Aldrich-McKelvey scaling model to estimate ideal points for French political data, capturing the political preferences and scaling them accordingly.