klaR Analyzing German Business Cycles

Weihs, Claus; Ligges, Uwe; Luebke, Karsten; Raabe, Nils

doi:10.1007/3-540-28397-8_36

Cited by 258 publications

(176 citation statements)

References 3 publications

Supporting

Mentioning

160

Contrasting

Unclassified

Order By: Relevance

“…Due to unequal group covariance matrices, a permutational MANOVA in the R package vegan (Oksanen et al 2010) was implemented. We used backward stepwise selection for quadratic discriminant analysis using the R package klaR (Weihs et al 2005) to arrive at the final set of discriminating variables between groups.…”

Section: Discussionmentioning

confidence: 99%

Variability of resource partitioning in sympatric tropical boobies

Kappes¹,

Weimerskirch²,

Pinaud³

et al. 2011

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

Inter-and intraspecific competition can lead to resource partitioning in sympatric species, processes likely affected by environmental productivity and population size. We investigated the foraging behaviour and diet of masked (Sula dactylatra) and red-footed (S. sula) boobies at Tromelin Island, western Indian Ocean, to examine the role of resource partitioning in the foraging strategies of these sympatric species in an extreme oligotrophic environment. We compared our results to published studies with differing environmental conditions or population sizes. We used GPS loggers and Argos transmitters to track foraging movements and used time-depth recorders to estimate dive depths. Masked boobies travelled further and at faster rates than redfooted boobies, and sexes did not differ in foraging behaviour. Based on randomization tests, the foraging range of each species (95% utilization distribution; UD) overlapped significantly. However, at core foraging areas (50% and 25% UD), interspecific segregation was greater than expected by chance alone. No intraspecific spatial segregation was detected between sexes. Environmental characteristics of area-restricted search zones differed between species, but not sexes; masked boobies utilized warmer, deeper, and less windy oceanic environments than red-footed boobies. Masked boobies attained greater diving depths than red-footed boobies and consumed primarily flying fish, whereas red-footed boobies consumed mostly squid. Red-footed and masked boobies breeding in the extreme oligotrophic environment near Tromelin demonstrated greater dietary partitioning differences in foraging ranges compared to a less oligotrophic environment. This suggests that environmental productivity may play a role in processes of resource partitioning in these sympatric species.KEY WORDS: Resource partitioning · GPS · Satellite tracking · Area-restricted search · Diet · Sula dactylatra · Sula sula Resale or republication not permitted without written consent of the publisher

show abstract

Section: Discussionmentioning

confidence: 99%

Variability of resource partitioning in sympatric tropical boobies

Kappes¹,

Weimerskirch²,

Pinaud³

et al. 2011

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

show abstract

“…The reason for this is that although bias can hurt the individual class density estimates, this might not influence the posterior probabilities as much, especially near the decision (Hastie et al, 2009). The naive Bayes method was implemented using the R package NaiveBayes (Weihs et al, 2005), using the standard parameters with activated usekernel parameter.…”

Section: Naive Bayesmentioning

confidence: 99%

Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification

Hauschild¹,

Baumbach²

2012

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. Exhaled air carries information on human health status. Ion mobility spectrometers combined with a multi-capillary column (MCC/IMS) is a well-known technology for detecting volatile organic compounds (VOCs) within human breath. This technique is relatively inexpensive, robust and easy to use in every day practice. However, the potential of this methodology depends on successful application of computational approaches for finding relevant VOCs and classification of patients into disease-specific profile groups based on the detected VOCs. We developed an integrated state-of-the-art system using sophisticated statistical learning techniques for VOC-based feature selection and supervised classification into patient groups. We analyzed breath data from 84 volunteers, each of them either suffering from chronic obstructive pulmonary disease (COPD), or both COPD and bronchial carcinoma (COPD + BC), as well as from 35 healthy volunteers, comprising a control group (CG). We standardized and ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 11 (3): 2733-2744 (2012) A.-C. Hauschild et al. 2734 integrated several statistical learning methods to provide a broad overview of their potential for distinguishing the patient groups. We found that there is strong potential for separating MCC/IMS chromatograms of healthy controls and COPD patients (best accuracy COPD vs CG: 94%). However, further examination of the impact of bronchial carcinoma on COPD/no-COPD classification performance is necessary (best accuracy CG vs COPD vs COPD + BC: 79%). We also extracted 20 high-scoring VOCs that allowed differentiating COPD patients from healthy controls. We conclude that these statistical learning methods have a generally high accuracy when applied to wellstructured, medical MCC/IMS data.

show abstract

“…We trained a SOM with a hexagonal and Gaussian neighbourhood with 20×20 cells with the matrix P as input data (Yan, 2004;Weihs et al, 2005). As mentioned before, all measures where transformed to a uniform distribution in the range [0, 1] in order to reduce effects from the differing distribution shapes and scales.…”

Section: Data Reduction With Sommentioning

confidence: 99%

Analysing the temporal dynamics of model performance for hydrological models

Reusser

Blume

Schaefli

et al. 2009

Hydrol. Earth Syst. Sci.

101

View full text Add to dashboard Cite

Abstract.The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.

show abstract

klaR Analyzing German Business Cycles

Cited by 258 publications

References 3 publications

Variability of resource partitioning in sympatric tropical boobies

Variability of resource partitioning in sympatric tropical boobies

Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification

Analysing the temporal dynamics of model performance for hydrological models

Contact Info

Product

Resources

About