| corr_iita {DAKS} | R Documentation |
Corrected Inductive Item Tree Analysis
Description
corr_iita performs the corrected inductive item tree analysis
procedure and returns the corresponding diff values.
Usage
corr_iita(dataset, A)
Arguments
dataset |
a required data frame or matrix consisting of binary,
|
A |
a required list of competing quasi orders (surmise
relations), for instance obtained from a call to
|
Details
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.
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 |
error.rate |
a vector of the error rates corresponding to the competing quasi orders in |
Note
The function iita can be used to perform one of the
three inductive item tree analysis procedures (including the
corrected inductive item tree analysis method) selectively. Whereas
for the function corr_iita a selection set of competing quasi
orders has to be passed via the argument A manually,
iita automatically generates a selection set from the data
using the inductive generation procedure implemented in
ind_gen.
The latter approach using iita is common so far, in
knowledge space theory, where the inductive data analysis methods
have been utilized for exploratory derivations of surmise relations
from data. The function corr_iita, on the other hand, can be
used to select among surmise relations for instance obtained from
querying experts or from competing psychological theories.
Author(s)
Anatol Sargin, Ali Uenlue
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;
mini_iita for minimized 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))
corr_iita(pisa, ind)