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ResourceSelection

Resource Selection (Probability) Functions for Use-Availability Data in R

https://github.com/psolymos/resourceselection

Science Score: 49.0%

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    Links to: arxiv.org
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    Low similarity (10.8%) to scientific vocabulary

Keywords

cran ecology estimation lele r rsf rspf solymos weighted-distributions
Last synced: 6 months ago · JSON representation

Repository

Resource Selection (Probability) Functions for Use-Availability Data in R

Basic Info
Statistics
  • Stars: 9
  • Watchers: 5
  • Forks: 4
  • Open Issues: 8
  • Releases: 2
Topics
cran ecology estimation lele r rsf rspf solymos weighted-distributions
Created over 11 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog

README.md

ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data

CRAN version CRAN RStudio mirror downloads check

Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006), Lele (2009), and Solymos & Lele (2016).

Install

CRAN version:

R install.packages("ResourceSelection")

Development version:

R devtools::install_github("psolymos/ResourceSelection")

User visible changes in the package are listed in the NEWS file.

Report a problem

Use the issue tracker to report a problem.

Example

```R

Some data processing

goats$exp.HLI <- exp(goats$HLI) goats$sin.SLOPE <- sin(pi * goats$SLOPE / 180) goats$ELEVATION <- scale(goats$ELEVATION) goats$ET <- scale(goats$ET) goats$TASP <- scale(goats$TASP)

Fit two RSPF models:

global availability (m=0) and bootstrap (B=99)

m1 <- rspf(STATUS ~ TASP + sin.SLOPE + ELEVATION, goats, m=0, B = 99) m2 <- rspf(STATUS ~ TASP + ELEVATION, goats, m=0, B = 99)

Inspect the summaries

summary(m1)

Call:

rspf(formula = STATUS ~ TASP + sin.SLOPE + ELEVATION, data = goats, m = 0,

B = 99)

Resource Selection Probability Function (Logistic RSPF) model

Non-matched Used-Available design

Maximum Likelihood estimates

with Nonparametric Bootstrap standard errors (B = 99)

Fitted probabilities:

Min. 1st Qu. Median Mean 3rd Qu. Max.

1.947e-08 4.280e-07 9.977e-07 1.376e-06 1.924e-06 8.793e-06

Coefficients (logit link):

Estimate Std. Error z value Pr(>|z|)

(Intercept) -16.89454 0.26284 -64.276 <2e-16 ***

TASP 0.39116 0.01396 28.011 <2e-16 ***

sin.SLOPE 5.36640 0.09740 55.098 <2e-16 ***

ELEVATION 0.09829 0.01165 8.439 <2e-16 ***

---

Signif. codes: 0 '**' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1

Log-likelihood: -5.729e+04

BIC = 1.146e+05

Hosmer and Lemeshow goodness of fit (GOF) test:

X-squared = 152.4, df = 8, p-value < 2.2e-16

summary(m2)

Call:

rspf(formula = STATUS ~ TASP + ELEVATION, data = goats, m = 0, B = 99)

Resource Selection Probability Function (Logistic RSPF) model

Non-matched Used-Available design

Maximum Likelihood estimates

with Nonparametric Bootstrap standard errors (B = 99)

Fitted probabilities:

Min. 1st Qu. Median Mean 3rd Qu. Max.

0.01194 0.58010 0.86180 0.73660 0.95710 0.99830

Coefficients (logit link):

Estimate Std. Error z value Pr(>|z|)

(Intercept) 1.62906 0.10110 16.11 <2e-16 ***

TASP 1.86071 0.07751 24.01 <2e-16 ***

ELEVATION 1.14338 0.08315 13.75 <2e-16 ***

---

Signif. codes: 0 '**' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1

Log-likelihood: -5.91e+04

BIC = 1.182e+05

Hosmer and Lemeshow goodness of fit (GOF) test:

X-squared = 174.3, df = 8, p-value < 2.2e-16

Compare models: looks like m1 is better supported

CAIC(m1, m2)

df CAIC

m1 4 114591.7

m2 3 118225.2

Visualize the relationships

plot(m1) mep(m1) # marginal effects similar to plot but with CIs kdepairs(m1) # 2D kernel density estimates plot(m2) kdepairs(m2) mep(m2) ```

Marginal effect plots

Scatterplot matrix with 2D kernel density estimates

References

Lele, S.R. (2009) A new method for estimation of resource selection probability function. Journal of Wildlife Management 73, 122--127. [link]

Lele, S. R. & Keim, J. L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology 87, 3021--3028. [link]

Solymos, P. & Lele, S. R. (2016) Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution 7, 196--205. [link, preprint]

Owner

  • Name: Peter Solymos
  • Login: psolymos
  • Kind: user
  • Location: Edmonton, Canada

Tech-bio-nerd

GitHub Events

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  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 1,198
  • Total Committers: 4
  • Avg Commits per committer: 299.5
  • Development Distribution Score (DDS): 0.112
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
psolymos p****s@e****b 1,064
Peter Solymos p****s@g****m 132
Auriel Fournier a****r@g****m 1
stefan7th s****h@e****b 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 18
  • Total pull requests: 2
  • Average time to close issues: 2 months
  • Average time to close pull requests: about 4 hours
  • Total issue authors: 7
  • Total pull request authors: 2
  • Average comments per issue: 0.83
  • Average comments per pull request: 1.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
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  • Issue authors: 0
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  • Merged pull requests: 0
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  • Bot pull requests: 0
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Top Authors
Issue Authors
  • psolymos (12)
  • wienold (1)
  • CamilaDeu (1)
  • coverton-usgs (1)
  • indenkun (1)
  • psecekartal (1)
  • KathyGCY (1)
Pull Request Authors
  • psolymos (1)
  • aurielfournier (1)
Top Labels
Issue Labels
new feature (4) enhancement (3) bug (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 7,085 last-month
  • Total docker downloads: 42,043
  • Total dependent packages: 7
  • Total dependent repositories: 12
  • Total versions: 18
  • Total maintainers: 1
cran.r-project.org: ResourceSelection

Resource Selection (Probability) Functions for Use-Availability Data

  • Versions: 18
  • Dependent Packages: 7
  • Dependent Repositories: 12
  • Downloads: 7,085 Last month
  • Docker Downloads: 42,043
Rankings
Downloads: 4.6%
Dependent packages count: 6.4%
Dependent repos count: 8.4%
Average: 10.7%
Forks count: 12.3%
Docker downloads count: 14.1%
Stargazers count: 18.4%
Maintainers (1)
psolymos@gmail.com
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 2.13.0 depends
  • MASS * imports
  • Matrix * imports
  • pbapply * imports