Estimate the M2 fit statistic for diagnostic classification models

For diagnostic classification models, the M₂ statistic is calculated as described by Hansen et al. (2016) and Liu et al. (2016).

Usage

# S3 method for measrdcm
fit_m2(model, ..., ci = 0.9, force = FALSE)

Arguments

model: An estimated diagnostic classification model.
...: Unused, for extensibility.
ci: The confidence interval for the RMSEA.
force: If the M₂ has already been saved to the model object with add_fit(), should it be recalculated. Default is FALSE.

Value

A data frame created by dcm2::fit_m2().

Methods (by class)

fit_m2(measrdcm): M₂ for diagnostic classification models.

References

Hansen, M., Cai, L., Monroe, S., & Li, Z. (2016). Limited-information goodness-of-fit testing of diagnostic classification item response models. British Journal of Mathematical and Statistical Psychology, 69(3), 225-252. doi:10.1111/bmsp.12074

Liu, Y., Tian, W., & Xin, T. (2016). An application of M₂ statistic to evaluate the fit of cognitive diagnostic models. Journal of Educational and Behavioral Statistics, 41(1), 3-26. doi:10.3102/1076998615621293

Examples

rstn_mdm_lcdm <- measr_dcm(
  data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
  resp_id = "respondent", item_id = "item", type = "lcdm",
  method = "optim", seed = 63277, backend = "rstan"
)

fit_m2(rstn_mdm_lcdm)
#> # A tibble: 1 × 8
#>       m2    df  pval rmsea ci_lower ci_upper `90% CI`     srmsr
#>    <dbl> <int> <dbl> <dbl>    <dbl>    <dbl> <chr>        <dbl>
#> 1 0.0219     1 0.882     0        0    0.110 [0, 0.1102] 0.0456