Summarize the results of
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A numeric matrix of posterior draws as returned by
Arguments to be passed to or from other methods
A logical vector of length one; if
This function provides the posterior mean, median, standard deviation,
and 0.025 and 0.975 quantiles for GGUM parameters from posterior samples
Please note that the quantiles are calculated using the type 8 algorithm
from Hyndman and Fan (1996), as suggested by Hyndman and Fan (1996), rather
than the type 7 algorithm that would be the default from R's
Before calling this function, care should be taken to ensure that
post-processing has been done if necessary to identify the correct
reflective posterior mode, as discussed in the vignette and Duck-Mayr and
Please see the vignette (via
vignette("bggum")) for a full in-depth
practical guide to Bayesian estimation of GGUM parameters.
A list with three elements: estimates (a list of length four;
a numeric vector giving the means of the theta draws, a numeric vector
giving the means of the alpha draws, a numeric vector giving the means
of the delta draws, and a list where the means of the tau draws are
collated into a tau estimate vector for each item), sds (a list of length
four giving the posterior standard deviations for the theta, alpha, delta,
and tau draws), and statistics (a matrix with five columns and one row
for each parameter giving the 0.025 quantile, the 0.5 quantile, the mean,
the 0.975 quantile, and the standard deviation of the posterior draws
for each parameter; please note the quantiles are calculated using the
type 8 algorithm from Hyndman and Fan 1996, as suggested by Hyndman and
Fan 1996, rather than the type 7 algorithm that would be the default
object is a list and
a list of such lists will be returned.
Duck-Mayr, JBrandon, and Jacob Montgomery. 2019. “Ends Against the Middle: Scaling Votes When Ideological Opposites Behave the Same for Antithetical Reasons.” http://jbduckmayr.com/papers/ggum.pdf.
Hyndman, R. J. and Fan, Y. 1996. “Sample Quantiles in Packages.” American Statistician 50, 361–365.
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## NOTE: This is a toy example just to demonstrate the function, which uses ## a small dataset and an unreasonably low number of sampling interations. ## For a longer practical guide on Bayesian estimation of GGUM parameters, ## please see the vignette ( via vignette("bggum") ). ## We'll simulate data to use for this example: set.seed(123) sim_data <- ggum_simulation(100, 10, 2) ## Now we can generate posterior draws ## (for the purposes of example, we use 100 iterations, ## though in practice you would use much more) draws <- ggumMC3(data = sim_data$response_matrix, n_temps = 2, sd_tune_iterations = 100, temp_tune_iterations = 100, temp_n_draws = 50, burn_iterations = 100, sample_iterations = 100) ## Then post-process the output processed_draws <- post_process(sample = draws, constraint = which.min(sim_data$theta), expected_sign = "-") ## And now we can obtain a summary of the posterior posterior_summary <- summary(processed_draws) ## It contains all the parameter estimates str(posterior_summary$estimates) ## As well as the posterior standard deviations str(posterior_summary$sds) ## And a matrix of the mean (estimates), median, standard deviations, ## and 0.025 and 0.975 quantiles head(posterior_summary$statistics)
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