Model-Based Recursive Partitioning

Zeileis, Achim; Hothorn, Torsten; Hornik, Kurt

doi:10.1198/106186008x319331

Cited by 585 publications

(634 citation statements)

References 41 publications

Supporting

Mentioning

629

Contrasting

Unclassified

Order By: Relevance

“…In addition, tree models do not assume a linear relationship between predictors and the dependent variable and they are very useful for modelling higher-order interaction effects between predictor variables automatically. For this study we used a particular family of tree models called conditional inference trees that combine the rigorous theory of permutation statistics (Hothorn, Hornik, & Zeileis, 2006) with the principle of recursive partitioning (Zeileis, Hothorn, & Hornik, 2008).…”

Section: Exploratory Analysis (Regression Tree Model)mentioning

confidence: 99%

The Repeated Recording Illusion

Anglada-Tort

Müllensiefen

2017

Music Perception

View full text Add to dashboard Cite

The repeated recording illusion refers to the phenomenon in which listeners believe to hear different musical stimuli while they are in fact identical. The present paper aims to construct an experimental paradigm to enable the systematic measurement of this phenomenon, investigating potentially related extrinsic and individual difference factors. Participants were told to listen to “different” musical performances of an original piece when in fact they were exposed to the same repeated recording. Each time, the recording was accompanied by a text suggesting a low, medium, or high prestige of the performer. Most participants (75%) believed that they had heard different musical performances. Participants with high levels of neuroticism and openness were significantly more likely to fall for the illusion. While the explicit information presented with the music influenced participants’ ratings significantly, the effect of repeated exposure was only significant in the more familiar music condition. These results suggest that like many other human judgments, evaluations of music also rely on cognitive biases and heuristics that do not depend on the stimuli themselves. The repeated recording illusion can constitute a useful paradigm for investigating nonmusical factors because it allows for the study of their effects while the music remains the same.

show abstract

Section: Exploratory Analysis (Regression Tree Model)mentioning

confidence: 99%

The Repeated Recording Illusion

Anglada-Tort

Müllensiefen

2017

Music Perception

View full text Add to dashboard Cite

show abstract

“…Repeat steps 1-3 recursively in the resulting nodes until there are no more significant instabilities (or the number of data sets left in a node falls below a given stopping value). The statistical framework employed for testing the significance of instabilities in the model parameters is described in detail in [19] and [24]. The rationale of the underlying tests for parameter instability is that the individual deviations from a joint model are considered over the range of each potential splitting variable: As illustrated in Figure 4, the strength parameter for algorithm a k may show a systematic change when considered over the range of the characteristic c j -such as the first canonical correlation, that indicates linear separability -while over the range of other characteristics the parameter may vary only randomly.…”

Section: Preference Scalingmentioning

confidence: 99%

“…The rationale of the underlying tests for parameter instability is that the individual deviations from a joint model are considered over the range of each potential splitting variable: As illustrated in Figure 4, the strength parameter for algorithm a k may show a systematic change when considered over the range of the characteristic c j -such as the first canonical correlation, that indicates linear separability -while over the range of other characteristics the parameter may vary only randomly. Statistical tests are available to detect such systematic parameter instabilities and select the splitting variable or characteristic inducing the strongest instability [19,24].…”

Section: Preference Scalingmentioning

confidence: 99%

(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms

Eugster

Leisch

Strobl

2014

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

It is common knowledge that certain characteristics of data sets -such as linear separability or sample sizedetermine the performance of learning algorithms. In this paper we propose a formal framework for investigations on this relationship.The framework combines three, in their respective scientific discipline well-established, methods. Benchmark experiments are the method of choice in machine and statistical learning to compare algorithms with respect to a certain performance measure on particular data sets. To realize the interaction between data sets and algorithms, the data sets are characterized using statistical and information-theoretic measures; a common approach in the field of meta learning to decide which algorithms are suited to particular data sets. Finally, the performance ranking of algorithms on groups of data sets with similar characteristics is determined by means of recursively partitioning Bradley-Terry models, that are commonly used in psychology to study the preferences of human subjects. The result is a tree with splits in data set characteristics which significantly change the performances of the algorithms. The main advantage is the automatic detection of these important characteristics.The framework is introduced using a simple artificial example. Its real-word usage is demonstrated by means of an application example consisting of thirteen wellknown data sets and six common learning algorithms. All resources to replicate the examples are available online.

show abstract

“…Aiming to identify a lung miRNA expression signature predictive of ALK, EGFR, and KRAS mutational status, we used the Conditional Inference classification Trees (CTree) implemented in the Bioconductor package party (10); the prediction accuracy of the classification algorithm was estimated using 10-fold cross-validation (11).…”

Section: Alk- Egfr- Kras-drivenmentioning

confidence: 99%

microRNA classifiers are powerful diagnostic/prognostic tools in ALK- , EGFR- , and KRAS -driven lung cancers

Gasparini

Cascione

Landi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

microRNAs (miRNAs) can act as oncosuppressors or oncogenes, induce chemoresistance or chemosensitivity, and are major posttranscriptional gene regulators. Anaplastic lymphoma kinase (ALK), EGF receptor (EGFR), and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) are major drivers of non-small cell lung cancer (NSCLC). The aim of this study was to assess the miRNA profiles of NSCLCs driven by translocated ALK, mutant EGFR, or mutant KRAS to find driver-specific diagnostic and prognostic miRNA signatures. A total of 85 formalin-fixed, paraffin-embedded samples were considered: 67 primary NSCLCs and 18 matched normal lung tissues. Of the 67 primary NSCLCs, 17 were echinoderm microtubule-associated protein-like 4-ALK translocated (ALK + ) lung cancers; the remaining 50 were not (ALK − ). Of the 50 ALK − primary NSCLCs, 24 were EGFR and KRAS mutation-negative (i.e., WT; triple negative); 11 were mutant EGFR (EGFR + ), and 15 were mutant KRAS (KRAS + ). We developed a diagnostic classifier that shows how miR-1253, miR-504, and miR-26a-5p expression levels can classify NSCLCs as ALK-translocated, mutant EGFR, or mutant KRAS versus mutation-free. We also generated a prognostic classifier based on miR-769-5p and Let-7d-5p expression levels that can predict overall survival. This classifier showed better performance than the commonly used classifiers based on mutational status. Although it has several limitations, this study shows that miRNA signatures and classifiers have great potential as powerful, cost-effective next-generation tools to improve and complement current genetic tests. Further studies of these miRNAs can help define their roles in NSCLC biology and in identifying best-performing chemotherapy regimens.N on-small cell lung carcinoma (NSCLC) includes adenocarcinomas and squamous cell carcinomas (1), which mainly are treated surgically, although chemotherapy is used both preoperatively (neoadjuvant chemotherapy) and postoperatively (adjuvant chemotherapy), with or without radiotherapy (2, 3). The best-characterized oncogenes that drive NSCLC are anaplastic lymphoma kinase (ALK), EGF receptor (EGFR), the V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS), the protooncogene receptor tyrosine kinase MET, and the recently identified receptor tyrosine kinase of the insulin receptor family ROS1. Knowledge of their mutational status is of key importance in choosing the most efficient chemotherapy regimen (4).Recent clinical trials have shown that patients presenting with NSCLCs driven by EGFR and ALK gene rearrangements benefit from chemotherapies based on specific tyrosine kinase inhibitors (TKIs), such as EGFR-TKI and crizotinib, respectively. Despite the wide number of recently developed drugs, none targets mutant KRAS, although the inhibition of the downstream signaling component Ras-Raf-Erk seems to be a possible approach (2, 5). microRNAs (miRNAs) are small, noncoding RNAs that are deregulated in malignancies (5) and can act as oncogenes or oncosuppressors (6); their deregulation also can i...

show abstract

Model-Based Recursive Partitioning

Cited by 585 publications

References 41 publications

The Repeated Recording Illusion

The Repeated Recording Illusion

(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms

microRNA classifiers are powerful diagnostic/prognostic tools in ALK- , EGFR- , and KRAS -driven lung cancers

Contact Info

Product

Resources

About