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Binary Logistic Regression Model (binary + logistic_regression_model)
Selected AbstractsConidial dispersal of Gremmeniella abietina: climatic and microclimatic factorsFOREST PATHOLOGY, Issue 6 2003R.-L. Petäistö Summary The conidia dispersal started in Suonenjoki, in central Finland, in 1997,99 by the end of May or beginning of June, and continued occasionally at least to the middle of September. The temperature sum, day degrees (d.d., threshold temperature = 5°C) was between 100 and 165 d.d. at the beginning of dispersal. In years 1997,99, 80, 94 and 82% of the dispersal had occurred by the end of July , beginning of August when the temperature sum reached 800 d.d. All the spore data are coming from the spore traps. Cumulative number of conidia increased linearily with logarithm of temperature sum. A binary logistic regression model with temperature sum and rainfall as explanatory variables predicted accurately the date of the first spores in the spring: the prediction error was at most 3 days. The model classified 69% of all the days in the analysis correctly to the spore-free days and correspondingly 74% to the days of at least one spore caught. A regression model for the number of spores per day explains 21, 5 and 51% of the within-season variation in 1997,99 (24, 37 and 62% on a logarithmic scale). The explanatory weather variables in the model were d.d., rain and year. The very low explanatory coefficient of determination in 1998 results from one exceptionally high number of conidia. The between-differences in the total number of spores were large and could not be explained by the measured weather variables. In the regression model, these differences were taken into account by adding a constant for each year in the model. Rain increased conidia dispersal significantly but conidia were found also in consecutive rainless days. Résumé La dissémination des conidies a démarré fin mai-début juin à Suonenjoki, dans le centre de la Finlande, pour les années 1997,1999, et s'est poursuivie occasionnellement au moins jusqu'à mi-septembre. La somme de température (seuil de 5°C) est de 100-165 degrés-jours au début de la période de dissémination. Pour les années 1997, 1998 et 1999, 80%, 94% et 82% de la dissémination s'était produite fin juillet-début août, quand la somme de température avait atteint 800 degrés-jours. Le nombre cumulé de conidies augmente linéairement avec le logarithme de la somme de températures. Un modèle de régression logistique binaire avec la somme de températures et les précipitations comme variables explicatives prédit de façon précise la date des premières émissions au printemps: l'erreur de prédiction est au plus de trois jours. Le modèle assigne correctement 69% de l'ensemble des jours analysés à des jours sans spores et 74% des jours avec au moins une spore piégée. Un modèle de régression pour le nombre de spores par jour explique 21%, 5 % et 51 % de la variation intra-saison en 1997, 1998 et 1999 (24, 37 et 62% pour une échelle logarithmique). Les variables climatiques explicatives du modèle sont les degrés-jours, les précipitations et l'année. Le très faible coefficient de détermination de 1998 provient d'un seul comptage exceptionnellement élevé de conidies. Les différences entre années pour le nombre total de spores sont importantes et ne peuvent s'expliquer par les variables climatiques mesurées. Dans le modèle de régression, ces différences sont prises en compte en ajoutant une constante pour chaque année. Les pluies augmentent significativement la dissémination des conidies mais des conidies ont été observées également dans des périodes de plusieurs jours consécutifs sans pluie. Zusammenfassung Die Ausbreitung der Konidien von Gremmeniella abietina begann in Suonenjoki, Zentralfinnland, in den Jahren 1997 , 1999 Ende Mai oder Anfang Juni und dauerte gelegentlich bis mindestens Mitte September an. Die Temperatursumme (Schwellenwert 5 °C) lag zu Beginn der Ausbreitung zwischen 100 und 165. Ende Juli bis Anfang August, wenn die Temperatursumme 800 erreicht hatte, war in den Jahren 1997, 1998 und 1999 jeweils 80%, 94% bzw. 82% der mit einer Sporenfalle erfassten Sporulation abgeschlossen. Die kumulierte Anzahl der Sporen nahm linear mit dem Logarithmus der Temperatursumme zu. Ein binäres logistisches Regressionsmodell mit der Temperatursumme und dem Niederschlag als erklärenden Variablen sagte das Datum der ersten Sporenfreisetzung im Frühjahr zutreffend voraus, der Vorhersagefehler lag hier bei höchstens drei Tagen. Das Modell klassifizierte 69% der Tage ohne Sporulation richtig und analog 74% der Tage mit zumindest einer erfassten Spore. Ein Regressionsmodell für die Anzahl Sporen pro Tag erklärte 21%, 5% und 51% der Variation innerhalb einer Saison für die Jahre 1997, 1998 und 1999 (24, 37 und 62% auf der Skala des natürlichen Logarithmus). Die erklärenden Wettervariablen in dem Modell waren Temperatursumme, Niederschlag und Jahr. Der sehr kleine Wert des erklärenden Koeffizienten für 1998 ist die Folge eines einzelnen Ereignisses mit ungewöhnlich hoher Sporenzahl. Innerhalb der Anzahl der Sporen waren die Unterschiede gross und konnten nicht mit den gemessenen Wetterdaten erklärt werden. Im Regressionsmodell wurden diese Unterschiede berücksichtigt, indem für jedes Jahr eine Konstante hinzugefügt wurde. Regen erhöhte die Konidienausbreitung signifikant, aber Sporen waren auch an den nachfolgenden regenfreien Tagen nachweisbar. [source] Managing the matrix for large carnivores: a novel approach and perspective from cheetah (Acinonyx jubatus) habitat suitability modellingANIMAL CONSERVATION, Issue 1 2006J. R. Muntifering Abstract Effective management within the human-dominated matrix, outside of formally protected areas, is of paramount importance to wide-ranging carnviores. For instance, the largest extant population of cheetahs Acinonyx jubatus currently persists on privately owned Namibian ranchlands, and provides an excellent case study to examine and design matrix conservation approaches. Although human-caused mortality is likely the principal threat to this population, ancedotal evidence suggests that ,bush encroachment', the widespread conversion of mixed woodland and savannah habitats to dense, Acacia -dominated thickets, is another probable threat. A better understanding of cheetah habitat use, outside of protected areas, could be used to directly influence habitat management strategies and design local restoration and conflict mitigation efforts. To identify specific habitat characteristics associated with cheetah use, we used radio-telemetry locations to identify areas used intensively by cheetahs on commercial Namibian farms. We then compared the habitat characteristics of these ,high-use' areas with adjacent ,low-use' areas. A binary logistic regression model correctly categorized 92% of plot locations as high or low use, and suggested that cheetahs may be utilizing ,rewarding patches' with better sighting visibility and greater grass cover. We discuss the possible reasons for kudu Tragelaphus strepsiceros, Namibian cheetahs' preferred prey, exhibiting significantly lower abundance in high-use areas. Using habitat characteristics to identify areas intensively utilized by cheetahs has important implications for guiding future habitat restoration and developing effective predator conflict mitigation efforts. [source] Comparison of Quantitative T-Wave Alternans Profiles of Healthy Subjects and ICD PatientsANNALS OF NONINVASIVE ELECTROCARDIOLOGY, Issue 2 2009B.Eng., Euler De Vilhena Garcia Ph.D., M.Sc. Background: Current relevance of T-wave alternans is based on its association with electrical disorder and elevated cardiac risk. Quantitative reports would improve understanding on TWA augmentation mechanisms during mental stress or prior to tachyarrhythmias. However, little information is available about quantitative TWA values in clinical populations. This study aims to create and compare TWA profiles of healthy subjects and ICD patients, evaluated on treadmill stress protocols. Methods: Apparently healthy subjects, not in use of any medication were recruited. All eligible ICD patients were capable of performing an attenuated stress test. TWA analysis was performed during a 15-lead treadmill test. The derived comparative profile consisted of TWA amplitude and its associated heart rate, at rest (baseline) and at peak TWA value. Chi-square or Mann-Whitney tests were used with p values , 0.05. Discriminatory performance was evaluated by a binary logistic regression model. Results: 31 healthy subjects (8F, 23M) and 32 ICD patients (10F, 22M) were different on baseline TWA (1 ± 2 ,V; 8 ± 9 ,V; p < 0.001) and peak TWA values (26 ± 13 ,V; 37 ± 20 ,V; p = 0,009) as well as on baseline TWA heart rate (79 ± 10 bpm; 67 ± 15 bpm; p < 0.001) and peak TWA heart rate (118 ± 8 bpm; 90 ± 17 bpm; p < 0.001). The logistic model yielded sensitivity and specificity values of 88.9% and 92.9%, respectively. Conclusions: Healthy subjects and ICD patients have distinct TWA profiles. The new TWA profile representation (in amplitude-heart rate pairs) may help comparison among different research protocols. [source] Characterization of freshwater pearl mussel (Margaritifera margaritifera) riverine habitat using River Habitat Survey dataAQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS, Issue 3 2003L.C. Hastie Abstract 1.The feasibility of using River Habitat Survey (RHS) data to describe freshwater pearl mussel (Margaritifera margaritifera) macrohabitat in the River Spey, north-east Scotland, was investigated. 2.Mussels were found to be positively associated with a number of RHS variables. These included: boulder/cobble river bed substrates, broken/unbroken standing waves (channel flow types), aquatic liverworts/mosses/lichens and broadleaf/mixed woodland/bankside tree cover. Negative associations with gravel-pebble/silt substrates and emergent reeds/sedges/herbs were also found. 3.Two binary logistic regression models, based on seven and four variables, respectively, were constructed in order to predict the presence/absence of mussels at any given site. Predictive success rates of 83% and 78% were achieved. 4.Another binary logistic regression model, based on four variables, was constructed in order to predict the occurrence of ,optimal' M. margaritifera habitat (overall mussel densities , 1 m,2). A predictive success rate of 83% was achieved. 5.The results indicate two potentially important applications of RHS for the conservation management of M. margaritifera: (1) for monitoring the effects of physical changes on extant mussel beds (and predicting their effects on mussel populations), and (2) for determining the habitat suitability of historically occupied sites for re-introductions. Copyright © 2003 John Wiley & Sons, Ltd. [source] | ||||||||||