Item fit statistics
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 class 'itemfit' extract(object, what, ...) ## S3 method for class '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:
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
extract: extract various elements from itemfit objects
summary: print summary information
Wenchao Ma, The University of Alabama, wenchao.ma@ua.edu
Jimmy de la Torre, The University of Hong Kong
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.
## Not run: 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 ## End(Not run)
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