Data are passed through filterGraph
and normalizeData
.
Then the ‘unidim_adapt’ model is fit to each item individually.
A larger varCorrection
will obtain a more accurate
scale
, but is also more likely to produce an intractable
model. A good compromise is between 5.0 and 9.0.
calibrateItems(
df,
iter = 2000L,
chains = 4L,
varCorrection = 5,
maxAttempts = 5L,
...
)
A data.frame (one row per item) with the following columns:
Name of the item
Number of iterations per chain
Number of divergent transitions observed after warmup
Number of times the treedepth was exceeded
Number of chains with low E-BFMI
Minimum effective number of samples across all parameters
Maximum Rhat across all parameters
Median marginal posterior of scale
Median variance of theta (latent scores)
a data frame with pairs of vertices given in columns pa1
and pa2
, and item response data in other columns
A positive integer specifying the number of iterations for each chain (including warmup).
A positive integer specifying the number of Markov chains.
A correction factor greater than or equal to 1.0
How many times to try re-running a model with more iterations.
Additional options passed to stan
.
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P. C. (2019). Rank-normalization, folding, and localization: An improved \(\widehat R\) for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
check_hmc_diagnostics
# \donttest{
result <- calibrateItems(phyActFlowPropensity) # takes more than 5 seconds
print(result)
# }
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