DAKS (version 2.1-3)

mini_iita: Minimized Corrected Inductive Item Tree Analysis

Description

mini_iita performs the minimized corrected inductive item tree analysis procedure and returns the corresponding diff values.

Usage

mini_iita(dataset, A)

Arguments

dataset

a required data frame or matrix consisting of binary, $1$ or $0$, numeric data.

A

a required list of competing quasi orders (surmise relations), for instance obtained from a call to ind_gen.

Value

If the arguments dataset and A are of required types, corr_iita returns a named list of the following components:

diff.value

a vector of the diff values corresponding to the competing quasi orders in A.

error.rate

a vector of the error rates corresponding to the competing quasi orders in A.

Details

Minimized corrected inductive item tree analysis is a data analysis method for deriving knowledge structures (more precisely, surmise relations) from binary data. Details on this procedure can be found in iita. The set of competing quasi orders is passed via the argument A, so any selection set of quasi orders can be used.

The set of competing quasi orders must be a list of objects of the class set. These objects (quasi orders) consist of $2$-tuples $(i, j)$ of the class tuple, where a $2$-tuple $(i, j)$ is interpreted as `mastering item $j$ implies mastering item $i$.'

The data must contain only ones and zeros, which encode solving or failing to solve an item, respectively.

References

Sargin, A. and Uenlue, A. (2009) Inductive item tree analysis: Corrections, improvements, and comparisons. Mathematical Social Sciences, 58, 376--392.

Uenlue, A. and Sargin, A. (2010) DAKS: An R package for data analysis methods in knowledge space theory. Journal of Statistical Software, 37(2), 1--31. URL http://www.jstatsoft.org/v37/i02/.

See Also

orig_iita for original inductive item tree analysis; corr_iita for corrected inductive item tree analysis; iita, the interface that provides the three inductive item tree analysis methods under one umbrella; pop_variance for population asymptotic variances of diff coefficients; variance for estimated asymptotic variances of diff coefficients; pop_iita for population inductive item tree analysis. See also DAKS-package for general information about this package.

Examples

ind <- ind_gen(ob_counter(pisa))
mini_iita(pisa, ind)