Calculate item fit statistics (Chen, de la Torre, & Zhang, 2013) and draw heatmap plot for item pairs

itemfit(GDINA.obj, person.sim = "post", p.adjust.methods = "holm", cor.use = "pairwise.complete.obs", digits = 4, N.resampling = NULL, randomseed = 123456) # S3 method for itemfit extract(object, what, ...) # S3 method for itemfit summary(object, ...)

GDINA.obj | An estimated model object of class |
---|---|

person.sim | Simulate expected responses from the posterior or based on EAP, MAP and MLE estimates. |

p.adjust.methods | p-values for the proportion correct, transformed correlation, and log-odds ratio
can be adjusted for multiple comparisons at test and item level. This is conducted using |

cor.use | how to deal with missing values when calculating correlations? This argument will be passed to |

digits | How many decimal places in each number? The default is 4. |

N.resampling | the sample size of resampling. By default, it is the maximum of 1e+5 and ten times of current sample size. |

randomseed | random seed; This is used to make sure the results are replicable. The default random seed is 123456. |

object | objects of class |

what | argument for S3 method |

... | additional arguments |

an object of class `itemfit`

consisting of several elements that can be extracted using
method `extract`

. Components that can be extracted include:

- p
the proportion correct statistics, adjusted and unadjusted p values for each item

- r
the transformed correlations, adjusted and unadjusted p values for each item pair

- logOR
the log odds ratios, adjusted and unadjusted p values for each item pair

- maxitemfit
the maximum proportion correct, transformed correlation, and log-odds ratio for each item with associated item-level adjusted p-values

`extract`

: extract various elements from`itemfit`

objects`summary`

: print summary information

Chen, J., de la Torre, J., & Zhang, Z. (2013). Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling.
*Journal of Educational Measurement, 50*, 123-140.

if (FALSE) { dat <- sim10GDINA$simdat Q <- sim10GDINA$simQ mod1 <- GDINA(dat = dat, Q = Q, model = "GDINA") mod1 itmfit <- itemfit(mod1) # Print "test-level" item fit statistics # p-values are adjusted for multiple comparisons # for proportion correct, there are J comparisons # for log odds ratio and transformed correlation, # there are J*(J-1)/2 comparisons itmfit # The following gives maximum item fit statistics for # each item with item level p-value adjustment # For each item, there are J-1 comparisons for each of # log odds ratio and transformed correlation summary(itmfit) # use extract to extract various components extract(itmfit,"r") mod2 <- GDINA(dat,Q,model="DINA") itmfit2 <- itemfit(mod2) #misfit heatmap plot(itmfit2) itmfit2 }