Description Usage Arguments Details Value Note Author(s) References See Also Examples
mini_iita
performs the minimized corrected inductive item
tree analysis procedure and returns the corresponding diff
values.
1  mini_iita(dataset, A)

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

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 2tuples (i, j) of the class
tuple
, where a 2tuple (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.
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 
The function iita
can be used to perform one of the
three inductive item tree analysis procedures (including the
minimized corrected inductive item tree analysis method)
selectively. Whereas for the function mini_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 mini_iita
, on the other hand, can be
used to select among surmise relations for instance obtained from
querying experts or from competing psychological theories.
Anatol Sargin, Ali Uenlue
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/.
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 DAKSpackage
for general information about
this package.
1 2  ind < ind_gen(ob_counter(pisa))
mini_iita(pisa, ind)

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