calibrateItems {pcFactorStan}

Determine the optimal scale constant for a set of items

Description

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.

Usage

calibrateItems(
  df,
  iter = 2000L,
  chains = 4L,
  varCorrection = 5,
  maxAttempts = 5L,
  ...
)

Arguments

df

a data frame with pairs of vertices given in columns pa1 and pa2, and item response data in other columns

iter

A positive integer specifying the number of iterations for each chain (including warmup).

chains

A positive integer specifying the number of Markov chains.

varCorrection

A correction factor greater than or equal to 1.0

maxAttempts

How many times to try re-running a model with more iterations.

...

Additional options passed to stan.

Value

A data.frame (one row per item) with the following columns:

item

Name of the item

iter

Number of iterations per chain

divergent

Number of divergent transitions observed after warmup

treedepth

Number of times the treedepth was exceeded

low_bfmi

Number of chains with low E-BFMI

n_eff

Minimum effective number of samples across all parameters

Rhat

Maximum Rhat across all parameters

scale

Median marginal posterior of scale

thetaVar

Median variance of theta (latent scores)

References

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.

See Also

check_hmc_diagnostics

Examples

result <- calibrateItems(phyActFlowPropensity)  # takes more than 5 seconds
print(result)