| Title: | Example Data for R Package 'vortexR' |
|---|---|
| Description: | Contains selected data from two publications, Campbell 'et' 'al'. (2016) <DOI:10.1080/14486563.2015.1028486> and 'Pacioni' 'et' 'al'. (2017) <DOI:10.1071/PC17002>. The data is provided both as raw outputs from the population viability analysis software 'Vortex' and packaged as R objects. The R package 'vortexR' uses the raw data provided here to illustrate its functionality of parsing raw 'Vortex' output into R objects. |
| Authors: | Carlo Pacioni [aut, cre], Florian W. Mayer [aut] |
| Maintainer: | Carlo Pacioni <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.5 |
| Built: | 2025-02-16 05:15:48 UTC |
| Source: | https://github.com/cran/vortexRdata |
A folder with Vortex outputs from Campbell et al (2016).
Several .dat and .stdat files.
Campbell et al. (2016). Assessing the economic benefits of starling detection and control to Western Australia. Australasian Journal of Environmental Management, 23, 81-99 DOI:10.1080/14486563.2015.1028486
campbell_dir <- system.file("extdata", "campbell", package="vortexRdata") cat("campbell example files:\n"); dir(campbell_dir)campbell_dir <- system.file("extdata", "campbell", package="vortexRdata") cat("campbell example files:\n"); dir(campbell_dir)
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
collate_dat.
a data.frame of 2904 observations of 68 variables.
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. DOI:10.1071/PC17002
data("pac.clas") head(pac.clas)data("pac.clas") head(pac.clas)
Data from Pacioni et al. (2017) - sensitivity test scenario
'ST_Classic' - were used to generate a look-up table
sizes using lookup_table.
A data.frame with 24 observations of 8 variables.
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. DOI:10.1071/PC17002
data("pac.clas.lookup") head(pac.clas.lookup)data("pac.clas.lookup") head(pac.clas.lookup)
Data from Pacioni et al. (2017) - sensitivity test scenario
'ST_Classic' - were used to calculate the harmonic mean of adults and population
sizes using Nadults.
A data.frame with 24 observations of 4 variables.
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. DOI:10.1071/PC17002
data("pac.clas.Nadults") head(pac.clas.Nadults)data("pac.clas.Nadults") head(pac.clas.Nadults)
Data from Pacioni et al. (2017) - sensitivity test scenario
'ST_Classic' - were used to calculate the effective population size
sizes using Ne.
A data.frame with 24 observations of 2 variables.
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. DOI:10.1071/PC17002
data("pac.clas.Ne") head(pac.clas.Ne)data("pac.clas.Ne") head(pac.clas.Ne)
Results of pairwise comparisons of simulation scenarios included
in the sensitivity test scenario 'ST_Classic' using pairwise.
A named list of 12 elements. See documentation for details.
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. DOI:10.1071/PC17002
data("pac.clas.pairw") head(pac.clas.pairw)data("pac.clas.pairw") head(pac.clas.pairw)
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 collate_dat.
A data.frame of 6171 observations of 68 variables.
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. DOI:10.1071/PC17002
data("pac.lhs") head(pac.lhs)data("pac.lhs") head(pac.lhs)
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 collate_run.
A named list of two data.frames:
run (153 obs, 7 var), lrun (153 obs, 8 var).
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. DOI:10.1071/PC17002
data("pac.run.lhs") head(pac.run.lhs)data("pac.run.lhs") head(pac.run.lhs)
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 collate_yr.
A named list of two elements:
all (8712 obs, 26 var), means (2904 obs, 25 var).
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. DOI:10.1071/PC17002
data("pac.yr") head(pac.yr)data("pac.yr") head(pac.yr)
A folder with Vortex outputs from Pacioni et al. (2017) used to run examples and Vortex project file. NOTE: these simulations are shorter than those presented in the paper (only 3 runs for 120 'Vortex-years').
One .xml file and several .run and .stdat files.
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. DOI:10.1071/PC17002
pacioni_dir <- system.file("extdata", "pacioni", package="vortexRdata") cat("pacioni example files:\n"); dir(pacioni_dir)pacioni_dir <- system.file("extdata", "pacioni", package="vortexRdata") cat("pacioni example files:\n"); dir(pacioni_dir)
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 collate_dat.
a data.frame with 1020 observations of 47 variables.
Campbell et al. (2016). Assessing the economic benefits of starling detection and control to Western Australia. Australasian Journal of Environmental Management, 23, 81-99 DOI:10.1080/14486563.2015.1028486
data("sta.evy5") head(sta.evy5)data("sta.evy5") head(sta.evy5)
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 collate_dat.
a data.frame with 1020 observations of 47 variables.
Campbell et al. (2016). Assessing the economic benefits of starling detection and control to Western Australia. Australasian Journal of Environmental Management, 23, 81-99 DOI:10.1080/14486563.2015.1028486
data("sta.evy5.b11") head(sta.evy5.b11)data("sta.evy5.b11") head(sta.evy5.b11)
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 collate_dat.
a data.frame with 1632 observations of 44 variables.
Campbell et al. (2016). Assessing the economic benefits of starling detection and control to Western Australia. Australasian Journal of Environmental Management, 23, 81-99 DOI:10.1080/14486563.2015.1028486
data("sta.main") head(sta.main)data("sta.main") head(sta.main)
vortexRdata provides real-world example data for vortexR in both
raw (Vortex output) and binary (R objects) form.
vortexR uses the raw data provided here to illustrate its capability
to parse raw Vortex output files into one R object.
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.
vortexR has a number of functions that are useful during the
development of a Vortex project and for its analysis after completion.
vortexR facilitates the creation of plots and
computation of basic statistics to inspect the effect of changes carried out
in the Vortex project. Once the Vortex project development is completed, the same
framework used in 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 published as web-pages.
The use of vortexR ensures reproducibility and standardises analytical
approaches in population viability analysis.
Campbell et al. (2016). Assessing the economic benefits of starling detection and control to Western Australia. Australasian Journal of Environmental Management, 23, 81-99 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. DOI:10.1071/PC17002