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).
CRAN version:
install.packages("ResourceSelection")
Development version:
devtools::install_github("psolymos/ResourceSelection")
User visible changes in the package are listed in the NEWS file.
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## 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)Scatterplot matrix with 2D kernel density estimates
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]
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