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WEBMulti-session data and models combine data from several sessions. Sometimes this is merely a convenience, but it also enables the fitting of models with parameter values that apply across sessions – data are then effectively pooled with respect to those parameters.
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WEBA SECR model combines a model for the point process (the state model) and a model for distance-dependent detection (the observation model). We obtain an unbiased estimate of population density (and other parameters) by jointly fitting the state and observation models.
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WEBThis document provides an overview of secr 4.6, an R package for spatially explicit capture–recapture analysis (SECR). It includes some background on SECR, an outline of the package, and a more detailed description of how models are implemented. See secr-tutorial.pdf for an introductory tutorial.
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session: Session Vector in secr: Spatially Explicit Capture …
WEBMay 29, 2024 · For multi-session models the detector array and mask are specified separately for each session. Group models are therefore generally simpler to implement. On the other hand, sessions offer more flexibility in defining and evaluating between-session models, including trend models.
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- Specifying a Dynamic Population can simulate a multi-session population with known between-session survival, recruit- sim.popn ment and movement probabilities. The parameter settings to achieve this are passed to sim.popn in its ‘details’ argument. Components of ‘details’ that are relevant to turnover are described below; see for others. sim.popn M...
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WEBMulti-threading is a way to use multiple processor cores to speed computation. secr 4.0 performs multi-threading with parallelFor in RcppParallel (Allaire et al. 2019). Multi-threading is used in computationally intensive functions, particularly likelihood evaluation in …
R: Spatially Explicit Capture-Recapture Models
WEBModels are fitted in secr by maximizing either the full likelihood or the likelihood conditional on the number of individuals observed (n). Conditional likelihood models are limited to homogeneous Poisson density, but allow continuous individual covariates for detection. A model fitted with secr.fit is an object of class secr. Generic methods ...
ms: Multi-session Objects in secr: Spatially Explicit Capture …
WEBOct 18, 2023 · Multi-session Objects. Description. Logical function to distinguish objects that span multiple sessions. Usage. ## Default S3 method: ms(object, ...) ## S3 method for class 'mask' ms(object, ...) ## S3 method for class 'secr' ms(object, ...) . Arguments. Details. The test applied varies with the type of object.
secr: sharefactorlevels – R documentation – Quantargo
WEBDescription. Factor covariates can give trouble in multi-session models if the levels differ among sessions. A warning is provided by verify.capthist. This function forces factor covariates to use the same levels. Usage. shareFactorLevels(object, columns = NULL, stringsAsFactors = TRUE) Arguments. Details.
secr package - RDocumentation
WEBDescription. Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood.
Estimating the density of a small population of leopards
WEBJan 8, 2021 · We employed multi-session maximum-likelihood SECR modeling to estimate the density of a small population of leopard in a mountainous environment surrounded by deserts in central Iran. During 6724 camera trap nights, we detected 8 and 5 independent leopards in 2012 and 2016 sessions, respectively.
Help in fitting a multi-session SECR model in R Studio - Google …
WEBFeb 28, 2021 · I have four trapping sessions (session 1 and 3 in rainforest, session 2 in wet-sclerophyll forest and session 4 in dry-sclerophyll forest) and there are 7-9 occasions in each session. I...
WEBFour real parameters are commonly modelled in secr: D (density), g0, sigma and z. Only the last three real parameters, the ones jointly defining detection probability as a function of location, can be estimated directly when the model …
secr.fit : Spatially Explicit Capture-Recapture - R Package …
WEBMay 29, 2024 · Description. Estimate animal population density with data from an array of passive detectors (traps) by fitting a spatial detection model by maximizing the likelihood. Data must have been assembled as an object of class capthist. Integration is by summation over the grid of points in mask . Usage.
secr 4.6 - spatially explicit capture–recapture in R
WEBThis document provides an overview of secr 4.6, an R package for spatially explicit capture–recapture analysis (SECR). It includes some background on SECR, an outline of the package, and a more detailed description of how models are implemented.
oSCR: a spatial capture–recapture R package for inference about …
WEBMay 8, 2019 · In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi-session sex-structured likelihood.
WEBA study may include multiple sessions. All detections are placed in one file and sessions are distinguished by the identifier in the first column. Animals sometimes die on capture or are removed during a session. Mark these detections …
secr: vignettes/secr-overview.Rmd - R Package Documentation
WEBMay 29, 2024 · This document provides an overview of secr 4.6, an R package for spatially explicit capture--recapture analysis (SECR). It includes some background on SECR, an outline of the package, and a more detailed description of how models are implemented.
secr app 1.4 - University of Otago
WEBFor multi-session data the displayed histogram is for the current session (see Array tab), unless 'Combine sessions' has been selected on the Options screen. When sessions are combined, the default is to include session-session movements.
join: Combine or Split Sessions of capthist Object in secr: …
WEBOct 18, 2023 · Make a single-session capthist object from a list of single-session objects, or a multi-session capthist object. Usage. . join(object, remove.dupl.sites = TRUE, tol = 0.001, sites.by.name = FALSE, drop.sites = FALSE, intervals = NULL, sessionlabels = NULL, . timevaryingcov = NULL) . . unjoin(object, intervals, ...) Arguments. Details. join.
Intelligent Workload Scheduling with Azure Kubernetes Fleet …
WEBMay 22, 2024 · With Fleet Manager, you can group and organize your AKS clusters, propagate Kubernetes configurations across clusters, and orchestrate multi-cluster load balancing scenarios. This demo will show new capabilities that intelligently schedule your workloads across clusters based on your chosen criteria. Add to schedule. Save to …
secr-package function - RDocumentation
WEBMar 1, 2024 · Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects.
secr-package : Spatially Explicit Capture-Recapture Models
WEBMay 29, 2024 · Details. Spatially explicit capture–recapture is a set of methods for studying marked animals distributed in space. Data comprise the locations of detectors (traps, searched areas, etc. described in an object of class ‘traps’), and the detection histories of individually marked animals.
WEBThe trapping sessions were only 4 weeks apart in spring 1995. We can further investigate session differences by fitting a session-specific model. The fastest way to fit fully session-specific models is to fit each session separately; lapply here applies secr.fit separately to each component of infraCH:
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