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

  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, ...)



An estimated model object of class GDINA


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


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 p.adjust function in stats, and therefore all adjustment methods supported by p.adjust can be used, including "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY". See p.adjust for more details. "holm" is the default.


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


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


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


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


objects of class itemfit for various S3 methods


argument for S3 method extract indicating what to extract; It can be "p" for proportion correct statistics, "r" for transformed correlations, logOR for log odds ratios and "maxitemfit" for maximum statistics for each item.


additional arguments


an object of class itemfit consisting of several elements that can be extracted using method extract. Components that can be extracted include:


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


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


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


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

Methods (by generic)

  • 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.

Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.


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 }