data_speaker

Usage of subject pronouns and its predictors; speaker level "adult" very frequent.

datasets

Optimization of conditional inference trees from the package 'party'
for classification and regression.
For optimization, the model space is searched for the best tree on the full sample by
means of repeated subsampling. Restrictions are allowed so that only trees are accepted
which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a).
The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs
& Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated
applications of PrInDT() for different percentages of the observations of the large and the
small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT())
allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld,
2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel
classification. In addition to these PrInDT() variants for classification, the function
PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT()
allows for a posterior analysis of the distribution of a specified variable in the terminal
nodes of a given tree.
In version 2, additionally structured sampling is implemented in functions PrInDTCstruc() and
PrInDTRstruc(). In these functions, repeated measurements data can be analyzed, too.
Moreover, multilabel 2-stage versions of classification and regression trees are
implemented in functions C2SPrInDT() and R2SPrInDT() as well as interdependent
multilabel models in functions SimCPrInDT() and SimRPrInDT(). Finally, for mixtures of
classification and regression models functions Mix2SPrInDT() and SimMixPrInDT() are
implemented. Most of these extensions of PrInDT are described in Buschfeld &
Weihs (2025Fc).
References:
-- Buschfeld, S., Weihs, C. (2025Fc) "Optimizing decision trees for the analysis of World Englishes
and sociolinguistic data", Cambridge Elements.
-- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in
Decision Trees (PrInDT) - a Linguistic Example" <doi:10.48550/arXiv.2103.02336>;
-- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT"
<doi:10.48550/arXiv.2103.14931>;
-- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation
in undersampling and prediction, and ranking of predictors in ensembles" <doi:10.48550/arXiv.2108.05129>.

Claus Weihs

PrInDT

Prediction and Interpretation in Decision Trees for
Classification and Regression

Sarah Buschfeld

Niklas Nitsch

data_speaker function

A data frame with 3370 observations and 6 columns<dl>
 <dt>class</dt>
<dd>subject pronoun realized? Factor (2 levels "zero","realized")</dd> <dt>AGE</dt>
<dd>age: Numerical (in months)</dd> <dt>ETHN_GROUP</dt>
<dd>ethnic group: Factor (3 levels "C","I","n_a") (anonymized)</dd> <dt>MLU</dt>
<dd>mean length of utterance: Factor (5 levels "1","2","3","adult","OL")</dd> <dt>PRN_TYPE</dt>
<dd>pronoun type: Factor (5 levels "dem","it_con","it_ex","it_ref","refer")</dd> <dt>SPEAKER</dt>
<dd>speaker: Factor (2 levels "adult","child")</dd>
</dl>

Format

Subject pronouns and a predictor with one very frequent level — data_speaker

A data frame with 3370 observations and 6 columns<dl>
 <dt>class</dt>
<dd>subject pronoun realized? Factor (2 levels "zero","realized")</dd>

 <dt>AGE</dt>
<dd>age: Numerical (in months)</dd>

 <dt>ETHN_GROUP</dt>
<dd>ethnic group: Factor (3 levels "C","I","n_a") (anonymized)</dd>

 <dt>MLU</dt>
<dd>mean length of utterance: Factor (5 levels "1","2","3","adult","OL")</dd>

 <dt>PRN_TYPE</dt>
<dd>pronoun type: Factor (5 levels "dem","it_con","it_ex","it_ref","refer")</dd>

 <dt>SPEAKER</dt>
<dd>speaker: Factor (2 levels "adult","child")</dd>


</dl>

data_speaker: Subject pronouns and a predictor with one very frequent level

Description

Usage

Arguments

Format