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

- GDINA.obj
An estimated model object of class

`GDINA`

- 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

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

`use`

when calling`stats::cor`

.- 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

`itemfit`

for various S3 methods- what
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:

- 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(itemfit)`

: extract various elements from`itemfit`

objects`summary(itemfit)`

: 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
}
```