Extract components of a `measrfit`

object.

Extract components of an estimated diagnostic classification model

## Arguments

- model
The estimated to extract information from.

- ...
Additional arguments passed to each extract method.

`ppmc_interval`

:For

`what = "odds_ratio_flags"`

and`what = "conditional_prob_flags"`

, the compatibility interval used for determining model fit flags to return. For example, a`ppmc_interval`

of 0.95 (the default) will return any PPMCs where the posterior predictive*p*-value (ppp) is less than 0.025 or greater than 0.975.`agreement`

:For

`what = "classification_reliability"`

, additional measures of agreement to include. By default, the classification accuracy and consistency metrics defined Johnson & Sinharay (2018) are returned. Additional metrics that can be specified to`agreement`

are Goodman & Kruskal's lambda (`lambda`

), Cohen's kappa (`kappa`

), Youden's statistic (`youden`

), the tetrachoric correlation (`tetra`

), true positive rate (`tp`

), and the true negative rate (`tn`

).For

`what = "probability_reliability"`

, additional measures of agreement to include. By default, the informational reliability index defined by Johnson & Sinharay (2020) is returned. Additional metrics that can be specified to`agreement`

are the point biserial reliability index (`bs`

), parallel forms reliability index (`pf`

), and the tetrachoric reliability index (`tb`

), which was originally defined by Templin & Bradshaw (2013).

- what
Character string. The information to be extracted. See details for available options.

## Value

The extracted information. The specific structure will vary depending on what is being extracted, but usually the returned object is a tibble with the requested information.

## Details

For diagnostic classification models, we can extract the following information:

`item_param`

: The estimated item parameters. This shows the name of the parameter, the class of the parameter, and the estimated value.`strc_param`

: The estimated structural parameters. This is the base rate of membership in each class. This shows the class pattern and the estimated proportion of respondents in each class.`prior`

: The priors used when estimating the model.`classes`

: The possible classes or profile patterns. This will show the class label (i.e., the pattern of proficiency) and the attributes included in each class.`class_prob`

: The probability that each respondent belongs to class (i.e., has the given pattern of proficiency).`attribute_prob`

: The proficiency probability for each respondent and attribute.`m2`

: The M_{2}fit statistic. See`fit_m2()`

for details. Model fit information must first be added to the model using`add_fit()`

.`rmsea`

: The root mean square error of approximation (RMSEA) fit statistic and associated confidence interval. See`fit_m2()`

for details. Model fit information must first be added to the model using`add_fit()`

.`srmsr`

: The standardized root mean square residual (SRMSR) fit statistic. See`fit_m2()`

for details. Model fit information must first be added to the model using`add_fit()`

.`ppmc_raw_score`

: The observed and posterior predicted chi-square statistic for the raw score distribution. See`fit_ppmc()`

for details. Model fit information must first be added to the model using`add_fit()`

.`ppmc_conditional_prob`

: The observed and posterior predicted conditional probabilities of each class providing a correct response to each item. See`fit_ppmc()`

for details. Model fit information must first be added to the model using`add_fit()`

.`ppmc_conditional_prob_flags`

: A subset of the PPMC conditional probabilities where the*ppp*is outside the specified`ppmc_interval`

.`ppmc_odds_ratio`

: The observed and posterior predicted odds ratios of each item pair. See`fit_ppmc()`

for details. Model fit information must first be added to the model using`add_fit()`

.`ppmc_odds_ratio_flags`

: A subset of the PPMC odds ratios where the*ppp*is outside the specified`ppmc_interval`

.`loo`

: The leave-one-out cross validation results. See`loo::loo()`

for details. The information criterion must first be added to the model using`add_criterion()`

.`waic`

: The widely applicable information criterion results. See`loo::waic()`

for details. The information criterion must first be added to the model using`add_criterion()`

.`pattern_reliability`

: The accuracy and consistency of the overall attribute profile classification, as described by Cui et al. (2012). Reliability information must first be added to the model using`add_reliability()`

.`classification_reliability`

: The classification accuracy and consistency for each attribute, using the metrics described by Johnson & Sinharay (2018). Reliability information must first be added to the model using`add_reliability()`

.`probability_reliability`

: Reliability estimates for the probability of proficiency on each attribute, as described by Johnson & Sinharay (2020). Reliability information must first be added to the model using`add_reliability()`

.

## Methods (by class)

`measr_extract(measrdcm)`

: Extract components of an estimated diagnostic classification model.

## References

Cui, Y., Gierl, M. J., & Chang, H.-H. (2012). Estimating
classification consistency and accuracy for cognitive diagnostic
assessment. *Journal of Educational Measurement, 49*(1), 19-38.
doi:10.1111/j.1745-3984.2011.00158.x

Johnson, M. S., & Sinharay, S. (2018). Measures of agreement to
assess attribute-level classification accuracy and consistency for
cognitive diagnostic assessments. *Journal of Educational Measurement,
55*(4), 635-664. doi:10.1111/jedm.12196

Johnson, M. S., & Sinharay, S. (2020). The reliability of the
posterior probability of skill attainment in diagnostic classification
models. *Journal of Educational and Behavioral Statistics, 45*(1), 5-31.
doi:10.3102/1076998619864550

Templin, J., & Bradshaw, L. (2013). Measuring the reliability of
diagnostic classification model examinee estimates. *Journal of
Classification, 30*(2), 251-275. doi:10.1007/s00357-013-9129-4

## 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"
)
measr_extract(rstn_mdm_lcdm, "strc_param")
#> # A tibble: 2 × 2
#> class estimate
#> <chr> <rvar[1d]>
#> 1 [0] 0.49 ± NA
#> 2 [1] 0.51 ± NA
```