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recursive partitioning on incomplete data using surrogate decisions and multiple imputation

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第6期56卷 1552-1565页

作者： Hapfelmeier, A. Hothorn, T. Ulm, K. Tech Univ Munich Inst Med Stat & Epidemiol D-81675 Munich Germany Univ Munich Inst Stat D-80539 Munich Germany

The occurrence of missing data is a major problem in statistical data analysis. All scientific fields and data of all kinds and size are touched by this problem. There is a number of ad-hoc solutions which unfortunately lead to a loss of power, biased inference, underestimation of variability and distorted relationships between variables. A more promising approach of rising popularity is multiple imputation by chained equations (MICE) also known as imputation by full conditional specification (FCS). Alternatives to imputation are given by methods with built-in procedures. These include recursive partitioning by classification and regression trees as well as corresponding Random Forests. However there is only few literature comparing the two approaches. Existing evaluations often lack generalizability due to restrictions on data structure and simulation schemes. The application of both methods to several kinds of data and different simulation settings is meant to improve and extend the comparative analyses. Classification and regression studies are examined. recursive partitioning is executed by two popular tree and one Random Forest implementation. Findings show that multiple imputation produces ambiguous performance results for both, simulated and real life data. Using surrogates instead is a fast and simple way to achieve performances which are only negligible worse and in many cases even superior. (C) 2012 Published by Elsevier B.V.

关键词： recursive partitioning Classification and regression trees Random Forests Multiple imputation MICE Surrogates

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recursive partitioning to reduce distortion

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IEEE TRANSACTIONS ON INFORMATION THEORY 1997年第4期43卷 1122-1133页

作者： Nobel, AB Department of Statistics North Carolina State University Chapel Hill NC USA

Adaptive partitioning of a multidimensional feature space plays a fundamental role in the design of data-compression schemes, Most partition-based design methods operate in an iterative fashion, seeking to reduce distortion at each stage of their operation by implementing a linear split of a selected cell, The operation and eventual outcome of such methods is easily described in terms of binary tree-structured vector quantizers. This paper considers a class of simple growing procedures for tree-structured vector quantizers, Of primary interest is the asymptotic distortion of quantizers produced by the unsupervised implementation of the procedures, It is shown that application of the procedures to a convergent sequence of distributions with a suitable limit yields quantizers whose distortion tends to zero. Analogous results are established for tree-structured vector quantizers produced from stationary ergodic training data, The analysis is applicable to procedures employing both axis-parallel and oblique splitting, and a variety of distortion measures, The results of the paper apply directly to unsupervised procedures that may be efficiently implemented on a digital computer.

关键词： data compression recursive partitioning tree-structured vector quantizers unsupervised procedures

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recursive partitioning to prioritize morphometric traits that separate Aspen specialist Chaitophorus aphid by species and stage

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INTERNATIONAL JOURNAL OF TROPICAL INSECT SCIENCE 2022年第1期42卷 941-946页

作者： Raizada, Richa Gaur, Rajarshi Kumar Albrectsen, Benedicte R. St Marys Sch Dept Sci Sikar 332301 Rajasthan India Deen Dayal Upadhyaya Gorakhpur Univ Dept Biotechnol Gorakhpur 273009 Uttar Pradesh India Umea Univ Umea Plant Sci Ctr Dept Plant Physiol SE-90187 Umea Sweden

Arthropod herbivore assemblages are used to gain insight into questions about evolution, ecology, diversity, and conservation. However, determination at the species level of small arthropods may be challenging risking underestimating diversity. Here we suggest morphometric analyses as a supplementary determination method, and we demonstrate its use for a study of Chaitophorus species collected from Aspen trees (Populus tremula). Although sampled as one colony, the aphids represented three species. Rearing the species separately allowed us to get estimates characteristic of the developmental stages from each of the three species for morphometric comparisons. recursive partitioning (RP) was used to create a decision tree for choice of morphometric parameters that with significance (p < 0.05) could determine the aphids by species and developmental stage;this insight could then be used as a key for determination. Eight of fifteen morphometric traits were selected by RP to be used in the key. Body length was responsible for nine splits and was consequently the more consistent morphometric trait used in the key.

关键词： Chaitophorus populeti C pentandrinus C vitellinae Populus tremula Diversity recursive partitioning

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recursive partitioning techniques for modeling irrigation behavior

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ENVIRONMENTAL MODELLING & SOFTWARE 2013年 47卷 207-217页

作者： Andriyas, Sanyogita McKee, Mac Utah State Univ Dept Civil & Environm Engn Logan UT 84322 USA Utah State Univ Dept Civil & Environm Engn Utah Water Res Lab Logan UT 84322 USA

Accurate forecasts of short-term irrigation demands can provide information useful for canal operators to manage water diversions and deliveries more efficiently. This can be accomplished by analyzing the actions of the farmers who make water use decisions. Readily available data on biophysical conditions in farmers' fields and the irrigation delivery system during the growing season can be utilized to anticipate irrigation water orders in the absence of any predictive socio-economic information that could be used to provide clues into future irrigation decisions. Decision classification and the common factors, form a basis for division of farmers into groups, which can be then used to make predictions of future decisions to irrigate. In this paper, we have implemented three tree algorithms, viz, classification and regression trees (CART), random forest (RF), and conditional inference trees (Ctree), to analyze farmers' irrigation decisions. These tools were then used to forecast future decisions. During the training process, the models inferred connections between input variables and the decision output. These variables were a time series of the biophysical conditions during the days prior to irrigation. Data from the Canal B region of the Lower Sevier River Basin, near the town of Delta, Utah were used. The main crops in the region are alfalfa, barley and corn. While irrigation practices for alfalfa are dependent on the timing of cuts, for barley and corn the critical crop growth stages are often used as indicators of farmer decisions to irrigate. Though all the models performed well in forecasting farmer decisions to irrigate, the best prediction accuracies by crop type were: 99.3% for alfalfa using all the three models;98.7% for barley, using the CART model;and 97.6% for corn, with Ctree approximately. Crop water use, which is the amount of water lost through evapotranspiration, was the prime factor across all the crops to prompt irrigation, which complies with t

关键词： Farmer Irrigation behavior recursive partitioning Trees Decision

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recursive partitioning analysis of μ-opiate receptor high throughput screening results

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2001年第1-2期12卷 181-212页

作者： Horvath, D CEREP F-92506 Rueil Malmaison France

recursive partitioning (RP) was used to analyze a heterogeneous data set of I-L receptor High Throughput Screening results of combinatorial libraries, lead optimization products and reference opiate ligands from literature. Different sets of molecular descriptors and various parameterization schemes have been systematically assessed in search for an optimal R-P tree, best discriminating between mu. receptor ligands and inactive molecules. This discriminating ability has been evaluated in terms of a quality criterion (representing an enrichment factor corrected by the retrieval rate of active compounds), for both the learning set-with and without performing a cross-validation test -and a distinct validation set. The non-linearity of the approach, as well as the very large number of degrees of freedom of the models, render the statistical analysis - in particular, the detection of overfitting - quite difficult. The advantages and disadvantages of the RP approach are discussed on hand of the comparative analysis of the performances of the models under the studied conditions. Eventually, the features highlighted by the R-P model as essential sources for the activity of compounds with respect to the mu receptor are analyzed in the light of commonly accepted mu, receptor binding hypotheses. It is shown that the R-P model considered to be "optimal" due to its simultaneous success in the cross-validation and validation simulations has been able to "discover" the existence of the two main ("morphine-like" and "meperidine-like") mu ligand families, represented as the two main "active nodes" of this tree.

关键词： recursive partitioning datamining mu opiate receptor pharmocophore model

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recursive partitioning for risk stratification in men undergoing repeat prostate biopsies

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CANCER 2005年第9期104卷 1911-1917页

作者： Garzotto, M Park, Y Mongoue-Tchokote, S Bledsoe, J Peters, L Blank, BH Austin, D Beer, TM Mori, M Portland VA Med Ctr Urol Sect Portland OR 97239 USA Oregon Hlth & Sci Univ Div Urol Portland OR 97201 USA Oregon Hlth & Sci Univ Dept Publ Hlth & Prevent Med Portland OR 97201 USA Oregon Hlth & Sci Univ Inst Canc Biostat Shared Resource Portland OR 97201 USA Oregon Hlth & Sci Univ Div Hematol & Med Oncol Portland OR 97201 USA

BACKGROUND. The Current study was performed to identify risk factors and risk groups for carcinoma detection in men undergoing repeat prostate biopsies. METHODS. The medical records of all men who had a negative initial prostate biopsy and underwent at least one repeat biopsy between 1992 and 2003 were reviewed to extract age, race, family history of prostate carcinoma, body mass index, referral indication, all prostate-specific antigen (PSA) values, digital rectal examination, PSA density (PSAD), the presence of a hypoechoic lesion, and the presence of high-grade prostatic intraepithelial neoplasia (HGPIN) oil initial biopsy. Risk factors for a Subsequent diagnosis of prostate carcinoma were identified using the log-rank test and a stepwise, stratified Cox regression model. Based oil the risk factors identified by Cox regression analysis, recursive partitioning was further used for risk stratification. RESULTS. A total of 373 patients underwent 975 biopsy procedures. During a median follow-Lip of 37.0 months, prostate carcinoma was detected in 107 of 373 patients (28.9%). Independent predictors of a positive biopsy (P < 0.05) were PSA doubling time (PSADT), PSAD, referral indication, the presence of HGPIN, patient age, and family history of prostate carcinoma. recursive partitioning identified 4 distinct risk groups that were characterized by their PSADT and PSAD and the presence of HGPIN and had estimated 2-year and 5-year carcinoma detection rates of 3 +/- 1% and 21 +/- 4%, 28 +/- 5% and 40 +/- 7%, 22 +/- 6% and 58 +/- 8%, and 66 +/- 9% and 100%, respectively. CONCLUSIONS. The authors identified a series of independent risk factors for prostate carcinoma detection after an initial negative prostate biopsy and characterized clinically meaningful and distinct patient risk groups. Despite a negative initial biopsy, patients with high-risk features remain at risk for the detection of prostate carcinoma.

关键词： prostate carcinoma repeat biopsy recursive partitioning prostate-specific antigen (PSA) PSA density

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recursive partitioning ANALYSIS OF PROGNOSTIC FACTORS FOR SURVIVAL IN PATIENTS WITH ADVANCED CANCER

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INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 2009年第4期73卷 1169-1176页

作者： Chow, Edward Abdolell, Mohamed Panzarella, Tony Harris, Kristin Bezjak, Andrea Warde, Padraig Tannock, Ian Sunnybrook Hlth Sci Ctr Odette Canc Ctr Dept Radiat Oncol Toronto ON M4N 3M5 Canada Univ Toronto Princess Margaret Hosp Univ Hlth Network Toronto ON Canada

Purpose: To construct a predictive model for survival of patients referred to the Rapid Response Radiotherapy Program using recursive partitioning (RP). Methods and Materials: We analyzed 16 factors characterizing patients with metastases at first referral to the Rapid Response Radiotherapy Program for palliative radiotherapy in 1999 for their effect on survival. They included age, primary cancer site, site of metastases, weight loss (>= 10% during the past 6 months), Karnofsky performance status (KPS), interval from the first diagnosis of cancer to the first consultation at the Rapid Response Radiotherapy Program, analgesic consumption within the previous 24 It, and the nine symptom scores from the Edmonton Symptom Assessment Scale. We used RP to develop a predictive model of survival for patients referred in 1999, followed by temporal validation using patients referred in 2000, and external validation using patients referred in 2002 to another institution. Results: The model was able to separate patients into three groups with different durations of survival that were defined by (1) KPS >60;(2) KPS <= 60 with bone metastases only;and (3) KPS <= 60 with other metastases. The model performed moderately well when applied to the validation sets, but a major limitation was that it led to an unequal distribution of patients, with a small proportion of patients in the intermediate group. Conclusion: We have demonstrated that RP can be used to predict the survival of patients with advanced cancer. However, this model has no advantages compared with our published prognostic models that use the survival prediction scores and number of risk factors. (C) 2009 Elsevier Inc.

关键词： Predictive model Survival recursive partitioning Advanced cancer Palliative radiotherapy

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recursive partitioning of resistant mutations for longitudinal markers based on a U-type score

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BIOSTATISTICS 2011年第4期12卷 750-762页

作者： Hu, Chengcheng Degruttola, Victor Univ Arizona Div Epidemiol & Biostat Mel & Enid Zuckerman Coll Publ Hlth Tucson AZ 85724 USA Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA

Development of human immunodeficiency virus resistance mutations is a major cause of failure of antiretroviral treatment. We develop a recursive partitioning method to correlate high-dimensional viral sequences with repeatedly measured outcomes. The splitting criterion of this procedure is based on a class of U-type score statistics. The proposed method is flexible enough to apply to a broad range of problems involving longitudinal outcomes. Simulation studies are performed to explore the finite-sample properties of the proposed method, which is also illustrated through analysis of data collected in 3 phase II clinical trials testing the antiretroviral drug efavirenz.

关键词： Antiretroviral drugs Longitudinal data recursive partitioning Repeated measurements Resistance mutations Tree method

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recursive partitioning analysis of complex disease pharmacogenetic studies. I. Motivation and overview

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PHARMACOGENOMICS 2005年第1期6卷 65-75页

作者： Young, SS Ge, NX Natl Inst Stat Sci Res Triangle Pk NC 27709 USA Aventis Pharmaceut Drug Discovery Biostat Bridgewater NJ 08807 USA

Identifying genetic variation predictive of important phenotypes, including disease susceptibility, drug efficacy, and adverse events, is a challenging task, and theory and computer science work is being carried out in an attempt to tackle this issue. For many important diseases, such as diabetes, schizophrenia, and depression, the etiology is complex;either the disease is a result of several multiple mechanisms or is caused by an interaction among multiple genes or gene-environment interactions, or both. There is a need for statistical methods to deal with the large, complex data sets that will be used to disentangle these diseases. Each putative genetic polymorphism can be tested for association sequentially. The most difficult problem, however, is the identification of combinations of polymorphisms or genetic markers with increased predictive characteristics. Data from clinical trials, where patients with a particular disease are treated with certain drugs, can be retrospectively assembled using a case-control design. Such data will typically include treatment assignment, demographics, medical history, and genotypes for a large number of genetic markers. The number of variables in such data is expected to be much larger than the number of subjects. This report focuses on some of the methods being employed to deal with this complex data and covers, in some detail, a data-mining method - recursive partitioning - to analyze such data. The methods are demonstrated using a complex simulated data set, as there are few available public data sets. This explication of recursive partitioning should provide researchers with a better idea of the current available analysis techniques, in order to allow them to plan their experiments more effectively.

关键词： complex disease pharmacogenctics recursive partitioning

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recursive partitioning Based Spatial Join Strategy Between OGC's Web Feature Services

Recursive Partitioning Based Spatial Join Strategy Between O...

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Lan Guiwen Huang Quanyi Wuhan Univ Sch Geodesy & Geomat Wuhan 430072 Peoples R China Tsinghua Univ Ctr Publ Safety Res Beijing Peoples R China

ISBN: (纸本)9781424421077

OGC-compliant(OGC, Open Geospatial Consortium Inc.) Web Feature Services (WFSs) have gradually become an important kind of spatial data sources over the Internet. In such a distributed environment, a user may issue a query that relates two spatial datasets that are provided by two services at different sites. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial datasets by different services are expensive in terms of transmission cost. This paper examines the problems of spatial joins between WFSs, based on the spatial semijoin strategy and partition based spatial join strategy, this paper presents a spatial join processing strategy between two OGC-compliant WFSs. The experimental result illustrates that when in the query window there are enough spatial features from the two related datasets, the proposed strategy can greatly reduce the expensive of transmission cost and thus time cost.

关键词： Web Feature Service spatial join query optimization recursive partitioning

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