Nothing
R/vortexR_pkg_data.R
In vortexR: Post Vortex Simulation Analysis
#------------------------------------------------------------------------------#
# Package info
#------------------------------------------------------------------------------#
#' vortexR: an R package for Post Vortex Simulation Analysis
#'
#' \code{vortexR} facilitates Post Vortex Simulation Analysis (PVSA) by offering
#' tools to collate multiple Vortex (v10) output files into one R object, generate
#' plots and conduct basic analysis (e.g. pairwise comparisons of scenarios) and
#' more advanced statistics such as fitting of a Generalised Linear Model (GLM)
#' to investigate the main and the interaction effects of the variables of
#' interest.
#'
#' \code{vortexR} has a number of functions that are useful during the
#' development of a Vortex project and for its analysis after completion.
#' \code{vortexR} makes it easy to automatise the creation of plots and
#' computation of basic statistics to inspect the effect of changes carried out
#' in the Vortex project. Once the project development is completed, the same
#' framework used in \code{vortexR} during the development of the project can be
#' refined and extended to include more advanced statistical analyses or can be
#' easily included in Markdown documents for the creation of reports (by
#' converting them into pdf) or update web-pages.
#'
#' The use of \code{vortexR} ensures reproducibility and standardises analytical
#' approaches in population viability analysis.
#'
#' @section Documentations:
#' Use \code{help(package = 'vortexR')} for a list of \code{vortexR} functions
#' and their specific documentations.
#'
#' A more detailed description of the package and functions can be opened with:
#' \code{vignette(package='vortexR', topic='User-guide')}.
#'
#' More vignettes may be come available in the future. Use
#' \code{vignette(package='vortexR')} to see all the available vignettes.
#'
#' @section Citation:
#' If you use \code{vortexR}, please cite:
#' Pacioni, C., and Mayer, F. (2017). vortexR: an R package for post Vortex
#' simulation analysis. Methods in Ecology and Evolution.
#'
#' @section Get in touch:
#' Please, use \url{https://github.com/carlopacioni/vortexR/issues} to report
#' any issues with vortexR. If unsure, or for feedback, contact me at:
#' carlo.pacioni 'at' gmail.com.
#'
#' @section Publications:
#' \emph{Below there are listed a few publications that used \code{vortexR}.}
#'
#' Campbell et al. (2016). Assessing the economic benefits of starling
#' detection and control to Western Australia. Australasian Journal of
#' Environmental Management, 23, 81-99.
#' \href{https://dx.doi.org/10.1080/14486563.2015.1028486}{DOI:10.1080/14486563.2015.1028486}
#'
#' Pacioni, C., Williams, M., Lacy RC, Spencer, P.B.S. and Wayne, A.F. (2017)
#' Predators and genetic fitness: key threatening factors for the conservation
#' of bettong species. Pacific Conservation Biology.
#' \href{https://doi.org/10.1071/PC17002}{DOI:10.1071/PC17002}
#'
#' @docType package
#'
#' @name vortexR
NULL
#------------------------------------------------------------------------------#
# Data
#------------------------------------------------------------------------------#
#' @name sta.main
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from the main Vortex scenarios used
#' in Campbell et al (2016). Vortex outputs, from the project named
#' 'Starlingv3PopBased' (.dat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.main
#' @template data-example
#' @format a \code{data.frame} with 1632 observations of 44 variables.
#' @template citation-campbell
NULL
#' @name sta.evy5
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from Vortex scenarios used in
#' Campbell et al (2016) to simulate major application of control measures
#' in every 5 year cycle. Vortex outputs, from the project named
#' 'Starlingv3PopBased' and the sensitivity test scenario 'MReductEvy5'
#' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.evy5
#' @template data-example
#' @format a \code{data.frame} with 1020 observations of 47 variables.
#' @template citation-campbell
NULL
#' @name sta.evy5.b11
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from Vortex scenarios used in Campbell
#' et al (2016) to simulate major application of control measures in every
#' 5 year cycle, maintaining 2011 levels of investment. Vortex outputs, from
#' the project named 'Starlingv3PopBased' and the sensitivity test scenario
#' 'MReduction_B11_09Evy5' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.evy5.b11
#' @template data-example
#' @format a \code{data.frame} with 1020 observations of 47 variables.
#' @template citation-campbell
NULL
#' @name pac.clas
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Subset (only 3 runs) of data from Pacioni et al. (2017) used
#' to conduct a sensitivity analysis on demographic parameters. Vortex
#' outputs, from the project named 'Pacioni_et_al' and (Single-Factor)
#' sensitivity test scenario 'ST_Classic' (.stdat files), were collated with
#' \code{collate_dat}.
#' @templateVar objname pac.clas
#' @template data-example
#' @format a \code{data.frame} of 2904 observations of 68 variables.
#' @template citation-pacioni
NULL
#' @name pac.lhs
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Latin Hypercube Sampling) sensitivity test scenario
#' 'ST_LHS' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname pac.lhs
#' @template data-example
#' @format A \code{data.frame} of 6171 observations of 68 variables.
#' @template citation-pacioni
NULL
#' @name pac.run.lhs
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Latin Hypercube Sampling) sensitivity test scenario
#' 'ST_LHS' (.run files), were collated with \code{collate_run}.
#' @templateVar objname pac.run.lhs
#' @template data-example
#' @format A named list of two \code{data.frame}s:
#' run (153 obs, 7 var), lrun (153 obs, 8 var).
#' @template citation-pacioni
NULL
#' @name pac.yr
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Single-Factor) sensitivity test scenario 'ST_Classic'
#' (.yr files), were collated with \code{collate_yr}.
#' @templateVar objname pac.yr
#' @template data-example
#' @format A named list of two \code{element}s:
#' all (8712 obs, 26 var), means (2904 obs, 25 var).
#' @template citation-pacioni
NULL
#' @name pac.clas.Nadults
#' @title Harmonic mean of adults and population sizes
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to calculate the harmonic mean of adults and population
#' sizes using \code{Nadults}.
#' @templateVar objname pac.clas.Nadults
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 4 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.Ne
#' @title Effective population size
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to calculate the effective population size
#' sizes using \code{Ne}.
#' @templateVar objname pac.clas.Ne
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 2 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.lookup
#' @title Look-up table
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to generate a look-up table
#' sizes using \code{lookup_table}.
#' @templateVar objname pac.clas.lookup
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 8 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.pairw
#' @title Results of pairwise comparisons of simulation scenarios
#' @description Results of pairwise comparisons of simulation scenarios included
#' in the sensitivity test scenario 'ST_Classic' using \code{pairwise}.
#' @templateVar objname pac.clas.pairw
#' @template data-example
#' @format A named list of 12 \code{element}s. See documentation for details.
#' @template citation-pacioni
NULL
Try the vortexR package in your browser
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vortexR documentation built on April 14, 2020, 7:23 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#------------------------------------------------------------------------------#
# Package info
#------------------------------------------------------------------------------#
#' vortexR: an R package for Post Vortex Simulation Analysis
#'
#' \code{vortexR} facilitates Post Vortex Simulation Analysis (PVSA) by offering
#' tools to collate multiple Vortex (v10) output files into one R object, generate
#' plots and conduct basic analysis (e.g. pairwise comparisons of scenarios) and
#' more advanced statistics such as fitting of a Generalised Linear Model (GLM)
#' to investigate the main and the interaction effects of the variables of
#' interest.
#'
#' \code{vortexR} has a number of functions that are useful during the
#' development of a Vortex project and for its analysis after completion.
#' \code{vortexR} makes it easy to automatise the creation of plots and
#' computation of basic statistics to inspect the effect of changes carried out
#' in the Vortex project. Once the project development is completed, the same
#' framework used in \code{vortexR} during the development of the project can be
#' refined and extended to include more advanced statistical analyses or can be
#' easily included in Markdown documents for the creation of reports (by
#' converting them into pdf) or update web-pages.
#'
#' The use of \code{vortexR} ensures reproducibility and standardises analytical
#' approaches in population viability analysis.
#'
#' @section Documentations:
#' Use \code{help(package = 'vortexR')} for a list of \code{vortexR} functions
#' and their specific documentations.
#'
#' A more detailed description of the package and functions can be opened with:
#' \code{vignette(package='vortexR', topic='User-guide')}.
#'
#' More vignettes may be come available in the future. Use
#' \code{vignette(package='vortexR')} to see all the available vignettes.
#'
#' @section Citation:
#' If you use \code{vortexR}, please cite:
#' Pacioni, C., and Mayer, F. (2017). vortexR: an R package for post Vortex
#' simulation analysis. Methods in Ecology and Evolution.
#'
#' @section Get in touch:
#' Please, use \url{https://github.com/carlopacioni/vortexR/issues} to report
#' any issues with vortexR. If unsure, or for feedback, contact me at:
#' carlo.pacioni 'at' gmail.com.
#'
#' @section Publications:
#' \emph{Below there are listed a few publications that used \code{vortexR}.}
#'
#' Campbell et al. (2016). Assessing the economic benefits of starling
#' detection and control to Western Australia. Australasian Journal of
#' Environmental Management, 23, 81-99.
#' \href{https://dx.doi.org/10.1080/14486563.2015.1028486}{DOI:10.1080/14486563.2015.1028486}
#'
#' Pacioni, C., Williams, M., Lacy RC, Spencer, P.B.S. and Wayne, A.F. (2017)
#' Predators and genetic fitness: key threatening factors for the conservation
#' of bettong species. Pacific Conservation Biology.
#' \href{https://doi.org/10.1071/PC17002}{DOI:10.1071/PC17002}
#'
#' @docType package
#'
#' @name vortexR
NULL
#------------------------------------------------------------------------------#
# Data
#------------------------------------------------------------------------------#
#' @name sta.main
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from the main Vortex scenarios used
#' in Campbell et al (2016). Vortex outputs, from the project named
#' 'Starlingv3PopBased' (.dat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.main
#' @template data-example
#' @format a \code{data.frame} with 1632 observations of 44 variables.
#' @template citation-campbell
NULL
#' @name sta.evy5
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from Vortex scenarios used in
#' Campbell et al (2016) to simulate major application of control measures
#' in every 5 year cycle. Vortex outputs, from the project named
#' 'Starlingv3PopBased' and the sensitivity test scenario 'MReductEvy5'
#' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.evy5
#' @template data-example
#' @format a \code{data.frame} with 1020 observations of 47 variables.
#' @template citation-campbell
NULL
#' @name sta.evy5.b11
#' @title Collated results from Vortex scenarios - Campbell et al (2016)
#' @description A dataset with the results from Vortex scenarios used in Campbell
#' et al (2016) to simulate major application of control measures in every
#' 5 year cycle, maintaining 2011 levels of investment. Vortex outputs, from
#' the project named 'Starlingv3PopBased' and the sensitivity test scenario
#' 'MReduction_B11_09Evy5' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname sta.evy5.b11
#' @template data-example
#' @format a \code{data.frame} with 1020 observations of 47 variables.
#' @template citation-campbell
NULL
#' @name pac.clas
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Subset (only 3 runs) of data from Pacioni et al. (2017) used
#' to conduct a sensitivity analysis on demographic parameters. Vortex
#' outputs, from the project named 'Pacioni_et_al' and (Single-Factor)
#' sensitivity test scenario 'ST_Classic' (.stdat files), were collated with
#' \code{collate_dat}.
#' @templateVar objname pac.clas
#' @template data-example
#' @format a \code{data.frame} of 2904 observations of 68 variables.
#' @template citation-pacioni
NULL
#' @name pac.lhs
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Latin Hypercube Sampling) sensitivity test scenario
#' 'ST_LHS' (.stdat files), were collated with \code{collate_dat}.
#' @templateVar objname pac.lhs
#' @template data-example
#' @format A \code{data.frame} of 6171 observations of 68 variables.
#' @template citation-pacioni
NULL
#' @name pac.run.lhs
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Latin Hypercube Sampling) sensitivity test scenario
#' 'ST_LHS' (.run files), were collated with \code{collate_run}.
#' @templateVar objname pac.run.lhs
#' @template data-example
#' @format A named list of two \code{data.frame}s:
#' run (153 obs, 7 var), lrun (153 obs, 8 var).
#' @template citation-pacioni
NULL
#' @name pac.yr
#' @title Collated results from Vortex scenarios - Pacioni et al. (2017)
#' @description Data from Pacioni et al. (2017) used to conduct a sensitivity
#' analysis on demographic parameters. Vortex outputs, from the project named
#' 'Pacioni_et_al' and (Single-Factor) sensitivity test scenario 'ST_Classic'
#' (.yr files), were collated with \code{collate_yr}.
#' @templateVar objname pac.yr
#' @template data-example
#' @format A named list of two \code{element}s:
#' all (8712 obs, 26 var), means (2904 obs, 25 var).
#' @template citation-pacioni
NULL
#' @name pac.clas.Nadults
#' @title Harmonic mean of adults and population sizes
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to calculate the harmonic mean of adults and population
#' sizes using \code{Nadults}.
#' @templateVar objname pac.clas.Nadults
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 4 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.Ne
#' @title Effective population size
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to calculate the effective population size
#' sizes using \code{Ne}.
#' @templateVar objname pac.clas.Ne
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 2 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.lookup
#' @title Look-up table
#' @description Data from Pacioni et al. (2017) - sensitivity test scenario
#' 'ST_Classic' - were used to generate a look-up table
#' sizes using \code{lookup_table}.
#' @templateVar objname pac.clas.lookup
#' @template data-example
#' @format A \code{data.frame} with 24 observations of 8 variables.
#' @template citation-pacioni
NULL
#' @name pac.clas.pairw
#' @title Results of pairwise comparisons of simulation scenarios
#' @description Results of pairwise comparisons of simulation scenarios included
#' in the sensitivity test scenario 'ST_Classic' using \code{pairwise}.
#' @templateVar objname pac.clas.pairw
#' @template data-example
#' @format A named list of 12 \code{element}s. See documentation for details.
#' @template citation-pacioni
NULL
Try the vortexR package in your browser
Any scripts or data that you put into this service are public.
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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