Search Results for "linear"

Identification of clines from allele frequency or genome-wide databases. Angular transformation. Sigmoid function. Parameterization of a cline: orientation, Pearson’s product-moment correlation coefficient, linear and sigmoid regression and coefficient of determination. Graphical representation of the cline. Confidence limits. Spatial autocorrelation. Moran's index. Isolation by distance. Exponential regression. Correlation Fst/Distance. Centroid method. Cline's expected vs. predicted heterozygosity. MDS. Sammon projection. Geographical distances. ...

Metamodels provide a novel approach to species risk assessment in which diverse threats are addressed through different disciplines, act at different spatiotemporal scales, or interact in non-linear ways. A metamodel links multiple, discrete models that depict components of a complex system, governing the flow of information between models and the sequence of events in the simulation. Each discrete model simulates processes specific to its disciplinary realm while being informed of changes in other parts of the metamodel by accessing common descriptors of the system, populations, and individuals. ...

Starting from GIMMS values as ascii data (each line will be treated as individual time series), the application can smooth the ts - values by applying different algorithm that are based on Fouriertransformation. Separation of seasonal figure as well as the detection of linear trends is possible. Possible breaks in longterm mean can be detected with a change-point analysis using CuSum algorithms. Phenological events such as start-of-season, day-of-max/day-of-min, end-of-season can be determined via threshold-based algorithms or via analysis of max. increase of NDVI during green-up. These analyses work also for multimodal phenologies where individual seasons (partly) interfere each other. ...

...It was named after the REDD+ pilot-project 'programme holistique de conservation des forêts à Madagascar'. phcfM includes two main functions: (i) demography(), to model the population growth with time in a hierarchical Bayesian framework using population census data and Gaussian linear mixed models and (ii) deforestation(), to model the deforestation process in a hierarchical Bayesian framework using land-cover change data and Binomial logistic regression models with variable time-intervals between land-cover observations. The two functions use embedded Gibbs samplers written in C++ with the Scythe statistical library to reduce computational time.

WHATCH’EM simulates the highly non-linear manner in which atmospheric conditions and container characteristics determine water temperature and height, leading to results that are not always intuitive and likely not simulated by simpler empirical models. WHATCH’EM has the capacity to be utilized for other mosquitoes and other container-inhabiting arthropods.

The R script was written to perform and compare several versions of cluster algorithms as described in: Seifert, Ritz & Csősz (2013) published in Myrmecological News. The aim is to identify (cryptic) taxa based on numeric data on morphology. The first step is a Linear Discriminant Analysis (LDA) based on subunits (ant nests) independent of any taxon information. Following analysis are Non-Metric Multidimensional Scaling (NMDS) and k-Means clustering. The NMDS scores are also summarized using K-Means. For comparison, k-Means is also run on the nest means (without preceeding LDA). Cluster assignment is summarized in an output file for all three methods.

Simulates data from a gaussian/beta/piecewise-linear species response function on an environmental gradient, with different sampling (error) distributions. See "Cline (biology)" on wikipedia.org.