diff --git a/DESCRIPTION b/DESCRIPTION index 73145e5..98aeef6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,7 @@ Description: A simple way of fitting detection functions to distance sampling Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and for example analyses. -Version: 2.0.0.9001 +Version: 2.0.0.9002 URL: https://github.com/DistanceDevelopment/Distance/ BugReports: https://github.com/DistanceDevelopment/Distance/issues Language: en-GB diff --git a/_pkgdown.yml b/_pkgdown.yml index 4a1b272..9eabe3f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,6 +1,7 @@ url: ~ template: bootstrap: 5 + math-rendering: mathjax bslib: bg: "#fcfaf2" fg: "#14059e" @@ -120,6 +121,8 @@ navbar: href: articles/web-only/CTDS/camera-distill.html - text: Alternative optimiser href: articles/web-only/alt-optimise/mcds-dot-exe.html + - text: Predict detection probabilities + href: articles/web-only/predict_detprob/predicting_detection_probabilities.html - text: News href: news/index.html diff --git a/docs/404.html b/docs/404.html index 85b4c72..7c6f906 100644 --- a/docs/404.html +++ b/docs/404.html @@ -11,7 +11,7 @@ - + @@ -22,7 +22,7 @@ Distance - 2.0.0 + 2.0.0.9001
  • News
    • @@ -75,7 +76,7 @@

    Analysis of camera trapping data

    Eric Howe, Eric Rexstad and Len Thomas

    - CREEM, Univ of St Andrews

    November 2024

    + CREEM, Univ of St Andrews

    December 2024

    Source: vignettes/web-only/CTDS/camera-distill.Rmd
    camera-distill.Rmd
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    + + +
    +
    + +

    Predict detection probability at given distance

    +

    Eric Rexstad

    +
    + CREEM, Univ of St Andrews

    December 2024

    + + Source: vignettes/web-only/predict_detprob/predicting_detection_probabilities.Rmd +
    predicting_detection_probabilities.Rmd
    +
    +
    + + + +
    +

    Motivation +

    +

    Having fitted a detection function model to distance sampling data, often there is interest in using the fitted model to predict detection probability for combinations of predictors (including distance).

    +

    Commonly this is done when covariates other that distance are included in the model. For example, researchers may want to know the difference in predicted detection probability at a given distance among different habitat types. It is this scenario that lead to the development of this case study. Credit to Ward Langeraert for bringing this to our attention.

    +

    We construct a simple simulated line transect distance sampling survey, sampling a population consisting of four types: two sexes and two ages. Each of the four categories of animals have distinct simulated detection functions. We generate a single survey of this population and fit a detection function containing an agesex covariate, consistent with how the simulated data were generated.

    +

    The main focus of this case study is interrogating the fitted model to a) visualise the fitted detection function model for each of the four age/sex classes and b) focus upon the differences in predicted detection probability between age/sex classes at a specified perpendicular distance. However, the application of the approach demonstrated in this case study is not limited only to those situations.

    +
    +
    +

    Simulate a data set +

    +

    The simulation of a data set is accomplished using the dsims package Marshall (2024). Details of the dsims package will not be described here, interested readers should follow the previous link.

    + +
    ## Loading required package: dssd
    +
    +set.seed(19511)
+study.region <- make.region()
+density <- make.density(region = study.region)
+covariate.list <- list()
+# The population equal proportion in four age/sex classes
+covariate.list$agesex <- list(data.frame(level = c("youngmale", "youngfemale", "adultmale", "adultfemale"), 
+                                          prob = rep(0.25,4)))
+pop.desc <- make.population.description(region = study.region, 
+                                        density = density, 
+                                        N = 400,
+                                        covariates = covariate.list,
+                                        fixed.N = TRUE)
+cov.params <- list()
+truncation_distance <- 50
+cov.params$agesex = data.frame(level = c("youngmale", "youngfemale", "adultmale", "adultfemale"), 
+                            param = c(0.2, 0.7, 1.0, 1.3))
+detect.cov <- make.detectability(key.function = "hn" ,
+                                 scale.param = 10,
+                                 cov.param = cov.params, 
+                                 truncation = truncation_distance)
+plot(detect.cov, pop.desc, lwd=3)
    +
    + +Simulated detection functions for four age x sex classes.

    +Figure 1: Simulated detection functions for four age x sex classes. +

    +
    +
    +design <- make.design(region = study.region,
+                      transect.type = "line",
+                      design = "systematic",
+                      spacing = 100,
+                      truncation = truncation_distance)
+analysis.cov <- make.ds.analysis(dfmodel = list(~agesex),
+                                 key = "hn",
+                                 truncation = truncation_distance)
+my.simulation <- make.simulation(reps = 999, 
+                                 design = design,
+                                 population.description = pop.desc,
+                                 detectability = detect.cov,
+                                 ds.analysis = analysis.cov)
+my.survey <- run.survey(my.simulation)
+ds_model <- analyse.data(analysis.cov, my.survey)
    +

    The plot created in the above chunk shows the simulated detection functions for each age/sex class in the simulated population. The last object created in the above chunk is of type dsmodel, equivalent to objects created by the ds() function in the Distance package. It is this object (ds_model) that you would have created in your analysis and want to use to predict detection probabilities from the fitted model.

    +
    +
    +

    Interrogate ds_model$ddf +

    +

    To produce our predicted detection probabilities, we need merely one component of the ds_model object. It is called ddfobj and it is located deep within ds_model, specificially ds_model$ddf$ds$aux$ddfobj

    +

    The function that does the work of producing estimated detection probabilities from a ddfobj along with a detection distance and an index to the set of predictor covariates is called detfct in the mrds package.

    +

    Code below isolated the ddfobj within ds_model just to make life easier. Then the index of the first observation of each age/sex combination is found within the design matrix (because the design matrix is formed after any truncation takes place in model fitting).

    +
    +ddfobj <- ds_model$ddf$ds$aux$ddfobj
+ym.indx <- which(ddfobj$xmat$agesex=="youngmale")[1]
+yf.indx <- which(ddfobj$xmat$agesex=="youngfemale")[1]
+am.indx <- which(ddfobj$xmat$agesex=="adultmale")[1]
+af.indx <- which(ddfobj$xmat$agesex=="adultfemale")[1]
    +

    This completes the preliminaries to use detfct for predicting detection probabilities.

    +
    +

    Predict detection probability at a specific distance +

    +

    The next code chunk uses detfct to predict detection probabilities for each age/sex class at a single perpendicular distance.

    +
    +mydist <- 35
+Young_male <- mrds::detfct(mydist, ddfobj, index = ym.indx)
+Young_female <- mrds::detfct(mydist, ddfobj, index = yf.indx)
+Adult_male <- mrds::detfct(mydist, ddfobj, index = am.indx)
+Adult_female <- mrds::detfct(mydist, ddfobj, index = af.indx)
+outtable <- data.frame(Age_sex_class=c("Young male", "Young female", "Adult male", "Adult female"),
+                       Predicted_p=c(Young_male, Young_female, Adult_male, Adult_female))
+knitr::kable(outtable, digits = 3, 
+             caption="Predicted detection probabilities at 35m")
    + + + + + + + + + + + + + + + + + + + + + + + + +
    +Table 1: Predicted detection probabilities at 35m
    Age_sex_classPredicted_p
    Young male0.014
    Young female0.059
    Adult male0.555
    Adult female0.439
    +
    +
    +
    +

    Compute detection probabilities at range of distances +

    +

    We will create a code chunk that duplicates the capability of the add_df_covar_line function found in the mrds package. The code plots a detection function for each of the four age/sex classes over a range of requested distances using the indices calculated in the previous chunk.

    +
    +prediction_distances <- seq(0, 50) 
+g_x_ym <- mrds::detfct(prediction_distances, ddfobj, index = ym.indx)
+g_x_yf <- mrds::detfct(prediction_distances, ddfobj, index = yf.indx)
+g_x_am <- mrds::detfct(prediction_distances, ddfobj, index = am.indx)
+g_x_af <- mrds::detfct(prediction_distances, ddfobj, index = af.indx)
    +

    The vectors of predicted detection probabilities are plotted in the following chunk. The last section of the chunk adds the calculated probabilities at the 35m distance to the plot.

    +
    +plot(ds_model, pl.col="white", cex=0.7)
+lines(prediction_distances, g_x_ym, col="darkgreen", lwd=2)
+lines(prediction_distances, g_x_yf, col = "green", lwd=2)
+lines(prediction_distances, g_x_am, col = "darkblue", lwd=2)
+lines(prediction_distances, g_x_af, col = "brown", lwd=2)
+legend("topright", title="Age/sex class", lwd=2,
+       legend=c("young male", "young female", "adult male", "adult female"),
+       col=c("darkgreen", "green", "darkblue", "brown"))
+#----------
+abline(v=mydist, lwd=3)
+segments(0, Young_male, mydist, Young_male, col="darkgreen")
+segments(0, Young_female, mydist, Young_female, col="green")
+segments(0, Adult_male, mydist, Adult_male, col="darkblue")
+segments(0, Adult_female, mydist, Adult_female, col="brown")
+text(0, Young_male, round(Young_male,3), adj=c(-.1, -.3), cex=0.7)
+text(0, Young_female, round(Young_female,3), adj=c(-.1, -.3), cex=0.7)
+text(0, Adult_male, round(Adult_male,3), adj=c(-.1, -.3), cex=0.7)
+text(0, Adult_female, round(Adult_female,3), adj=c(-.1, -.3), cex=0.7)
    +
    + +Predicted detection probabilities for four age x sex classes; with those at 35m highlighted.

    +Figure 2: Predicted detection probabilities for four age x sex classes; with those at 35m highlighted. +

    +
    +
    +
    +Marshall, L. (2024). Dsims: Distance sampling simulations. Retrieved from https://github.com/DistanceDevelopment/dsims +
    +
    +
    +
    +
    + + + +
    +

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    + + + + + + + diff --git a/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 0000000..0afc91e Binary files /dev/null and b/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-5-1.png new file mode 100644 index 0000000..522ae41 Binary files /dev/null and b/docs/articles/web-only/predict_detprob/predicting_detection_probabilities_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/articles/web-only/strata/strata-distill.html b/docs/articles/web-only/strata/strata-distill.html index f147899..35bb47f 100644 --- a/docs/articles/web-only/strata/strata-distill.html +++ b/docs/articles/web-only/strata/strata-distill.html @@ -11,7 +11,7 @@ - + Distance - 2.0.0 + 2.0.0.9001