Home About us Contact
|
Home About us Contact | ||
|
|
||
Residual Analysis (residual + analysis)
Selected AbstractsResidual analysis for spatial point processes (with discussion)JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 5 2005A. Baddeley Summary., We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity , plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q,Q -plots of the residuals are effective in diagnosing interpoint interaction. [source] Analysis of plant species diversity with respect to island characteristics on the Channel Islands, CaliforniaJOURNAL OF BIOGEOGRAPHY, Issue 3 2000Aaron Moody Abstract Aim Species richness of native, endemic, and exotic plant groups is examined relative to island area, disturbance history, geological history, and other physical characteristics. Of particular interest are the biogeographic factors that underlie (a) differences in species-area and species-isolation relationships between plant groups; and (b) adherence or departure of individual islands and/or plant groups from expected patterns. Location The eight Channel Islands lie along the continental margin between the U.S./Mexico border and Point Conception, CA. They range in size from 2.6 to 249 km2, and are located from 20 to 100 km off the coast. The islands are known for their high degree of plant endemism, and they have undergone a long history of human occupation by indigenous peoples, followed by over a century of intensive grazing and other biotic disturbances. Methods The study is based on linear regression and residual analysis. Cases where individual islands and/or specific plant groups do not adhere to patterns expected under species-area and species-isolation paradigms, are evaluated with respect to other island characteristics that are not captured by considering only island size and isolation. Results All three plant groups exhibit strong, positive relationships between species richness and island size. For native species, the variance that remains after consideration of island size is largely explained by island isolation. For exotic species, residuals from the species-area relationship are unrelated to isolation. For endemic species, residuals from the species-area relationship are negatively related to isolation. Several islands are outliers for endemic and exotic species, for which richness values are not explained by either island area or isolation. Main,conclusions Species-area and species-isolation relationships for native, endemic, and exotic plant groups differ in accordance with hypothesized differences in the biogeographic factors that govern species diversity for these three groups. Most notably, endemic richness increases with isolation, suggesting the influence of this variable on processes of speciation and relictualism. These general relationships persist despite a long and varied history of human activity on the islands. Analysis of residuals suggests that deviations from expected patterns correspond to island-specific biogeographic factors. It is hypothesized that primary among these factors are land-use history, island environmental characteristics, and community-type richness. [source] Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysisJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 7 2001Marco Henrique Terra In this article we discuss artificial neural networks-based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so-called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back-propagation algorithm and the RBFN is trained with a Kohonen self-organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3-joint planar manipulator. © 2001 John Wiley & Sons, Inc. [source] Endemic regions of the vascular flora of the peninsula of Baja California, MexicoJOURNAL OF VEGETATION SCIENCE, Issue 3 2007Hugo Riemann Abstract Question: Can we recognize areas of high endemism and high endemic richness, using data from collections, and what are the ecological variables that best explain these areas? Location: Peninsula of Baja California, Mexico. Methods: We analysed the distribution of 723 endemic vascular plants species along the peninsula of Baja California and neighbouring islands distributed in 218 cartographic cells 15' x 20' in size. By means of a residual analysis, we identified areas of significantly high endemic species richness, and we calculated the degree of endemicity (or rarity) in each cell by giving to each species a weight factor inversely proportional to the land area it covers. Results: Nine regions of high-endemicity and/or high endemic species richness were found. Discussion and conclusions: The analyses of rarity and endemic species richness showed two contrasting scenarios: High endemicity values in oceanic and sky islands accounts for a high number of species with a restricted distribution, promoted most likely by genetic isolation and high environmental heterogeneity. High endemic richness along the peninsular coast is related to ecotonal transition along vegetation types. After correcting for collection effort (i.e. the number of specimens collected within a cell), we found the phytogeographic region and altitudinal heterogeneity to be the variables that best predicted endemic richness. Both high endemism and high endemic richness have distinct geographic patterns within our study region. The nine endemic regions provide elements for priority definitions in future conservation programs. [source] QSAR Analysis of 2,3-Diaryl Benzopyrans/Pyrans as Selective COX-2 Inhibitors Based on Semiempirical AM1 CalculationsMOLECULAR INFORMATICS, Issue 8 2004Sivaprakasam Prasanna Abstract Quantitative structure-activity relationship (QSAR) analysis was performed on a combined series of 2,3 diaryl benzopyrans and pyrans for their cyclooxygenase-2 (COX-2) inhibition. QSAR investigations based on semiempirical, Austin Model-1 (AM1) calculations reveal that electronic and hydophobic interactions are primarily responsible for COX-2 enzyme-ligand interaction. The derived QSAR model aided by residual analysis demonstrated that the COX-2 inhibitory activity is highly correlated with the electronic descriptors, lowest unoccupied molecular orbital (ELUMO), Dipole-Z and hydrophobicity of the molecules. The conclusion can be drawn that more hydrophobic, electron-withdrawing substituents at 3rd aromatic ring of the lead structure improves activity. The lesser the Z component the ligand has, the more correct its orientation towards the COX-2 binding site. The derived QSAR model shows good internal (exemplified through leave one out-q2=0.786) and external (r=0.5737) predictive ability for a test set and can be used in designing better selective COX-2 inhibitors among these congeners in future. [source] Model-Checking Techniques Based on Cumulative ResidualsBIOMETRICS, Issue 1 2002D. Y. Lin Summary. Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes under the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided. [source] | ||||||||||