From 719e79907226302ae9d1157940d26f533d113292 Mon Sep 17 00:00:00 2001 From: Ben Tupper Date: Mon, 16 Oct 2023 10:12:59 -0400 Subject: [PATCH] render all --- covariates.qmd | 2 +- data/bkg/bkg-covariates.gpkg | Bin 4132864 -> 4132864 bytes data/bkg/buffered-polygon.gpkg | Bin 11526144 -> 11526144 bytes data/model/v1/v1.0/model_v1.0.rds | Bin 16858 -> 21155 bytes data/obs/obs-covariates.gpkg | Bin 1753088 -> 1753088 bytes docs/covariates.html | 2 +- .../figure-html/unnamed-chunk-10-1.png | Bin 158499 -> 0 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 176694 -> 0 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 183789 -> 0 bytes docs/modeling-01.html | 24 +- .../figure-html/unnamed-chunk-11-1.png | Bin 258600 -> 258862 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 167170 -> 168334 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 74843 -> 76830 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 58948 -> 61029 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 91465 -> 89246 bytes docs/search.json | 4 +- .../figure-html/unnamed-chunk-11-1.png | Bin 258600 -> 258862 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 167170 -> 168334 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 74843 -> 76830 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 58948 -> 61029 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 91465 -> 89246 bytes modeling.html | 377 ------------------ 22 files changed, 16 insertions(+), 393 deletions(-) delete mode 100644 docs/covariates_files/figure-html/unnamed-chunk-10-1.png delete mode 100644 docs/covariates_files/figure-html/unnamed-chunk-12-1.png delete mode 100644 docs/covariates_files/figure-html/unnamed-chunk-15-1.png delete mode 100644 modeling.html diff --git a/covariates.qmd b/covariates.qmd index 7e28539..c748a0c 100644 --- a/covariates.qmd +++ b/covariates.qmd @@ -12,7 +12,7 @@ Presence only modeling using [MaxEnt](https://biodiversityinformatics.amnh.org/o ## Loading data -We have two data sources to load: point observation data and rasterized environmental predicitor data. +We have two data sources to load: point observation data and rasterized environmental predictor data. ### Load the observation data We'll load in our [OBIS](https://obis.org/taxon/127405) observations as a flat table, and immediately filter the data to any occurring from 2000 to present. diff --git a/data/bkg/bkg-covariates.gpkg b/data/bkg/bkg-covariates.gpkg index d4dc68f9c4bb8880efcb46b2f426cdb0de2975c7..69802279aca525d912f95c501a355a53d91d70a2 100644 GIT binary patch delta 17529 zcmc(GXLJ)s)b?suh;FUv9ob;OG|TjkNFzNWy%7ZIU65X-t76lUX{IC741^*eMs#F) z2_T2`Dw2>!ikx1_`#fXv=Kb+~KRAbT>~rS1tGiR~+^`bx4JdW{FT|M}ykqj}_8Q>qJ;3Sg-Y>uZrVsNzPRtlpnLFcj`gu$3 zO`git8Mx!gG+ACt(=7kUvb!M8%CcS%f6KBu5F=#i zD2PH?O66a&v^K;KvV=;vEa?sLlPswVv0X|=LoASzJ`iCkq4Fzh$FJNAH;51oB{EwEV>6`oh%vvQ7Vf}i21VcK8T1c90W057G^@cEDNZ7K^9Q? zh%9ITF-qp&4{^K99}Mxm%%^gX%zFT$T;>gdc-ANL8bZ#Mxer3zEpw^$$XqIal{w7% zq0FH&Epw>+Mv9s99Vs3PQ6a^RA&$!Ihak#iwhQ7FnN8&inMLJNnMLJdnbibhrp$Z< 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diff --git a/docs/covariates.html b/docs/covariates.html index e64817b..4156726 100644 --- a/docs/covariates.html +++ b/docs/covariates.html @@ -251,7 +251,7 @@

2 Loading data

-

We have two data sources to load: point observation data and rasterized environmental predicitor data.

+

We have two data sources to load: point observation data and rasterized environmental predictor data.

2.1 Load the observation data

We’ll load in our OBIS observations as a flat table, and immediately filter the data to any occurring from 2000 to present.

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-pred  0.0001196393 0.1200618 0.2675931 0.3033565 0.4398977 0.8816952 11487
+              Min.    1st Qu.    Median      Mean  3rd Qu.      Max.  NA's
+pred  2.305125e-07 0.03167425 0.1225203 0.2072669 0.326378 0.9489527 11487
 dimension(s):
      from to offset delta refsys point                    values x/y
 x       1 74 -76.38  0.25 WGS 84 FALSE                      NULL [x]
@@ -462,16 +462,16 @@ 

+1 0.1104861 2019-05-01 2019-05-01 POINT (-67.32935 40.42509) +2 0.3843953 2019-03-01 2019-03-01 POINT (-74.41057 36.49908) +3 0.3466826 2019-12-01 2019-12-01 POINT (-75.3994 35.9457) +4 0.5219442 2019-06-01 2019-06-01 POINT (-75.10864 36.94806) +5 0.7742993 2019-04-01 2019-04-01 POINT (-74.49892 36.57275) +6 0.1499107 2019-09-01 2019-09-01 POINT (-75.5519 36.1854) +7 0.4467580 2019-09-01 2019-09-01 POINT (-73.6245 40.3317) +8 0.3129951 2019-04-01 2019-04-01 POINT (-69.04389 39.82132) +9 0.8267838 2019-04-01 2019-04-01 POINT (-74.59436 36.87291) +10 0.8267838 2019-04-01 2019-04-01 POINT (-74.45753 36.72279)

Finally we can build a table that merges the prediction with the labels. We are going to add the name of the month to group by that.

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We’ll do it two ways. First by simply feeding the input data used to create the model into the prediction. This might seems a bit circular, but it is perfectly reasonable to see how the model does on already labeled data. Second we’ll make a prediction for each month in 2020 using raster data.\n\n6.1 Predict with a data frame\nHere we provide a data frame, in our case the original input data, to the predict() function with type cloglog which transform the response value into the 0-1 range.\n\nprediction = predict(model, input_table, type = 'cloglog')\nhist(prediction, xlab = \"prediction\", main = \"Basic Model\")\n\n\n\n\n\n6.1.1 How did it do?\nWe can use some utilities in the maxnetic package to help us assess the model. The pAUC() function will compute statistics, include a presence-only AUC value. We need to pass it two items - the universe of predictions and the predictions for just the presence points.\n\nix = input_vector > 0\npauc = maxnetic::pAUC(prediction, prediction[ix])\nplot(pauc, title = \"v1.0 Basic Model\")\n\n\n\n\nOverall, this is telling us that the model isn’t especially strong as a prediction tool, but it is much better than a 50-50 guess (that’s when AUC is close to 0.5, and the curve follows the light grey line). Learn more about ROC and AUC here.\n\n\n\n6.2 Predict with rasters\nWe can also predict using raster inputs using our basic model. Let’s read in rasters for each month of 2019, and then run a prediction for each month.\nWe provide a function read_predictors() that will read and bind the rasters together for you given the filtered databases and paths. So, first we define the paths and filter the databases to point to just the months in 2019.\n\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date) |>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\npredictors = read_predictors(sst_db = sst_db,\n u_wind_db = u_wind_db,\n v_wind_db = v_wind_db)\npredictors\n\nstars object with 3 dimensions and 3 attributes\nattribute(s):\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\nsst -1.558928 12.528449 19.5220385 17.6005908 23.501083 29.216452 11352\nu_wind -2.692028 1.144244 2.7007004 2.7228278 4.115177 13.148120 7612\nv_wind -5.431324 -1.411349 -0.3202622 -0.1398384 1.106175 4.636874 7612\ndimension(s):\n from to offset delta refsys point values x/y\nx 1 74 -76.38 0.25 WGS 84 FALSE NULL [x]\ny 1 46 46.12 -0.25 WGS 84 FALSE NULL [y]\ntime 1 12 NA NA Date NA 2019-01-01,...,2019-12-01 \n\n\nYou can see that we have the rasters in one object of three attributes (sst, u_wind and v_wind) each with 12 layers (Jan 2019 - Dec 2019). Now we can run the prediction.\n\npred = predict(model, predictors, type = 'cloglog')\npred\n\nstars object with 3 dimensions and 1 attribute\nattribute(s):\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\npred 0.0001196393 0.1200618 0.2675931 0.3033565 0.4398977 0.8816952 11487\ndimension(s):\n from to offset delta refsys point values x/y\nx 1 74 -76.38 0.25 WGS 84 FALSE NULL [x]\ny 1 46 46.12 -0.25 WGS 84 FALSE NULL [y]\ntime 1 12 NA NA Date NA 2019-01-01,...,2019-12-01 \n\n\nSince we get a spatially mapped prediction back, we can plot it.\n\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n sf::st_crop(pred)\n\nWarning: attribute variables are assumed to be spatially constant throughout\nall geometries\n\nplot_coast = function() {\n plot(sf::st_geometry(coast), col = 'green', add = TRUE)\n}\nplot(pred, hook = plot_coast)\n\n\n\n\nWell, that certainly looks appealing with higher likelihood of near shore observations occurring during the warmer months.\n\n6.2.1 How did it do?\nTo compute an ROC and AUC for each month, we have a little bit of work to do. We need to extract the observations locations for each month from the prediction maps. These we can then plot.\n\n\n\n\n\n\nNote\n\n\n\nWe have to modify the date for each point to be the first date of each month. That’s because our predictors are monthlies.\n\n\n\ntest_obs = obs |>\n dplyr::filter(dplyr::between(date, dates[1], dates[2])) |>\n dplyr::select(dplyr::all_of(\"date\")) |>\n dplyr::mutate(date = oisster::current_month(date))\n\nx = stars::st_extract(pred, test_obs, time_column = 'date') |>\n print()\n\nSimple feature collection with 612 features and 3 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: -75.7589 ymin: 35.1211 xmax: -65.48274 ymax: 43.83954\nGeodetic CRS: WGS 84\nFirst 10 features:\n pred time date geometry\n1 0.2759255 2019-05-01 2019-05-01 POINT (-67.32935 40.42509)\n2 0.7245142 2019-03-01 2019-03-01 POINT (-74.41057 36.49908)\n3 0.6664676 2019-12-01 2019-12-01 POINT (-75.3994 35.9457)\n4 0.4536477 2019-06-01 2019-06-01 POINT (-75.10864 36.94806)\n5 0.6864945 2019-04-01 2019-04-01 POINT (-74.49892 36.57275)\n6 0.3105710 2019-09-01 2019-09-01 POINT (-75.5519 36.1854)\n7 0.3874695 2019-09-01 2019-09-01 POINT (-73.6245 40.3317)\n8 0.3785449 2019-04-01 2019-04-01 POINT (-69.04389 39.82132)\n9 0.7447747 2019-04-01 2019-04-01 POINT (-74.59436 36.87291)\n10 0.7447747 2019-04-01 2019-04-01 POINT (-74.45753 36.72279)\n\n\nFinally we can build a table that merges the prediction with the labels. We are going to add the name of the month to group by that.\n\ny = x |>\n dplyr::mutate(month = factor(format(date, \"%b\"), levels = month.abb), \n .before = 1) |>\n dplyr::select(dplyr::all_of(c(\"month\", \"pred\", \"date\"))) |>\n dplyr::group_by(month) \n\ndplyr::count(y, month) |>\n print(n = 12)\n\nSimple feature collection with 12 features and 2 fields\nGeometry type: MULTIPOINT\nDimension: XY\nBounding box: xmin: -75.7589 ymin: 35.1211 xmax: -65.48274 ymax: 43.83954\nGeodetic CRS: WGS 84\n# A tibble: 12 × 3\n month n geometry\n * <fct> <int> <MULTIPOINT [°]>\n 1 Jan 21 ((-74.63902 36.26849), (-75.01758 36.49984), (-75.01801 36.72554…\n 2 Feb 7 ((-74.52432 37.24967), (-74.45561 37.16891), (-74.74373 36.72355…\n 3 Mar 23 ((-74.53117 36.26996), (-74.60195 36.72201), (-74.67127 36.72266…\n 4 Apr 169 ((-72.924 38.6733), (-73.0165 38.591), (-73.0036 38.56), (-73.10…\n 5 May 119 ((-74.56571 35.6059), (-75.2181 35.1934), (-75.3228 35.535), (-7…\n 6 Jun 53 ((-73.10608 38.72575), (-74.86204 36.27105), (-75.04656 36.34824…\n 7 Jul 48 ((-74.53554 36.19828), (-74.91756 36.27104), (-75.10905 36.27065…\n 8 Aug 39 ((-72.78628 38.68677), (-72.98868 38.61241), (-74.9889 36.2911),…\n 9 Sep 21 ((-75.3167 36.0439), (-75.5204 36.3294), (-75.5519 36.1854), (-7…\n10 Oct 79 ((-67.06445 42.91399), (-68.43614 43.83954), (-69.14391 43.16967…\n11 Nov 19 ((-72.52681 39.21286), (-71.54966 39.99385), (-67.79606 40.36107…\n12 Dec 14 ((-75.242 35.2705), (-75.3335 35.3027), (-75.436 35.1211), (-75.…\n\n\nNow how about one ROC plot for each month? Yikes! This requires a iterative approach, using group_map(), to compute the ROC for each month. We then follow with plot wrapping by the patchwork package.\n\npaucs = dplyr::group_map(y, \n function(tbl, key, pred_rasters = NULL){\n ix = key$month %in% month.abb\n x = dplyr::slice(pred_rasters, \"time\", ix)\n pauc = maxnetic::pAUC(x, tbl)\n plot(pauc,title = key$month, \n xlab = \"\", ylab = \"\")\n }, pred_rasters = pred)\n\npatchwork::wrap_plots(paucs, ncol = 4)\n\n\n\n\nHmmm. That’s surprising, yes? Why during the summer months does our AUC go down when we have the most number of observations? That seems counter intuitive." + "text": "6 Make a prediction\nNow we can make predictions with our basic model. We’ll do it two ways. First by simply feeding the input data used to create the model into the prediction. This might seems a bit circular, but it is perfectly reasonable to see how the model does on already labeled data. Second we’ll make a prediction for each month in 2020 using raster data.\n\n6.1 Predict with a data frame\nHere we provide a data frame, in our case the original input data, to the predict() function with type cloglog which transform the response value into the 0-1 range.\n\nprediction = predict(model, input_table, type = 'cloglog')\nhist(prediction, xlab = \"prediction\", main = \"Basic Model\")\n\n\n\n\n\n6.1.1 How did it do?\nWe can use some utilities in the maxnetic package to help us assess the model. The pAUC() function will compute statistics, include a presence-only AUC value. We need to pass it two items - the universe of predictions and the predictions for just the presence points.\n\nix = input_vector > 0\npauc = maxnetic::pAUC(prediction, prediction[ix])\nplot(pauc, title = \"v1.0 Basic Model\")\n\n\n\n\nOverall, this is telling us that the model isn’t especially strong as a prediction tool, but it is much better than a 50-50 guess (that’s when AUC is close to 0.5, and the curve follows the light grey line). Learn more about ROC and AUC here.\n\n\n\n6.2 Predict with rasters\nWe can also predict using raster inputs using our basic model. Let’s read in rasters for each month of 2019, and then run a prediction for each month.\nWe provide a function read_predictors() that will read and bind the rasters together for you given the filtered databases and paths. So, first we define the paths and filter the databases to point to just the months in 2019.\n\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date) |>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")|>\n dplyr::filter(dplyr::between(date, dates[1], dates[2]))\n\npredictors = read_predictors(sst_db = sst_db,\n u_wind_db = u_wind_db,\n v_wind_db = v_wind_db)\npredictors\n\nstars object with 3 dimensions and 3 attributes\nattribute(s):\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\nsst -1.558928 12.528449 19.5220385 17.6005908 23.501083 29.216452 11352\nu_wind -2.692028 1.144244 2.7007004 2.7228278 4.115177 13.148120 7612\nv_wind -5.431324 -1.411349 -0.3202622 -0.1398384 1.106175 4.636874 7612\ndimension(s):\n from to offset delta refsys point values x/y\nx 1 74 -76.38 0.25 WGS 84 FALSE NULL [x]\ny 1 46 46.12 -0.25 WGS 84 FALSE NULL [y]\ntime 1 12 NA NA Date NA 2019-01-01,...,2019-12-01 \n\n\nYou can see that we have the rasters in one object of three attributes (sst, u_wind and v_wind) each with 12 layers (Jan 2019 - Dec 2019). Now we can run the prediction.\n\npred = predict(model, predictors, type = 'cloglog')\npred\n\nstars object with 3 dimensions and 1 attribute\nattribute(s):\n Min. 1st Qu. Median Mean 3rd Qu. Max. NA's\npred 2.305125e-07 0.03167425 0.1225203 0.2072669 0.326378 0.9489527 11487\ndimension(s):\n from to offset delta refsys point values x/y\nx 1 74 -76.38 0.25 WGS 84 FALSE NULL [x]\ny 1 46 46.12 -0.25 WGS 84 FALSE NULL [y]\ntime 1 12 NA NA Date NA 2019-01-01,...,2019-12-01 \n\n\nSince we get a spatially mapped prediction back, we can plot it.\n\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n sf::st_crop(pred)\n\nWarning: attribute variables are assumed to be spatially constant throughout\nall geometries\n\nplot_coast = function() {\n plot(sf::st_geometry(coast), col = 'green', add = TRUE)\n}\nplot(pred, hook = plot_coast)\n\n\n\n\nWell, that certainly looks appealing with higher likelihood of near shore observations occurring during the warmer months.\n\n6.2.1 How did it do?\nTo compute an ROC and AUC for each month, we have a little bit of work to do. We need to extract the observations locations for each month from the prediction maps. These we can then plot.\n\n\n\n\n\n\nNote\n\n\n\nWe have to modify the date for each point to be the first date of each month. That’s because our predictors are monthlies.\n\n\n\ntest_obs = obs |>\n dplyr::filter(dplyr::between(date, dates[1], dates[2])) |>\n dplyr::select(dplyr::all_of(\"date\")) |>\n dplyr::mutate(date = oisster::current_month(date))\n\nx = stars::st_extract(pred, test_obs, time_column = 'date') |>\n print()\n\nSimple feature collection with 612 features and 3 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: -75.7589 ymin: 35.1211 xmax: -65.48274 ymax: 43.83954\nGeodetic CRS: WGS 84\nFirst 10 features:\n pred time date geometry\n1 0.1104861 2019-05-01 2019-05-01 POINT (-67.32935 40.42509)\n2 0.3843953 2019-03-01 2019-03-01 POINT (-74.41057 36.49908)\n3 0.3466826 2019-12-01 2019-12-01 POINT (-75.3994 35.9457)\n4 0.5219442 2019-06-01 2019-06-01 POINT (-75.10864 36.94806)\n5 0.7742993 2019-04-01 2019-04-01 POINT (-74.49892 36.57275)\n6 0.1499107 2019-09-01 2019-09-01 POINT (-75.5519 36.1854)\n7 0.4467580 2019-09-01 2019-09-01 POINT (-73.6245 40.3317)\n8 0.3129951 2019-04-01 2019-04-01 POINT (-69.04389 39.82132)\n9 0.8267838 2019-04-01 2019-04-01 POINT (-74.59436 36.87291)\n10 0.8267838 2019-04-01 2019-04-01 POINT (-74.45753 36.72279)\n\n\nFinally we can build a table that merges the prediction with the labels. We are going to add the name of the month to group by that.\n\ny = x |>\n dplyr::mutate(month = factor(format(date, \"%b\"), levels = month.abb), \n .before = 1) |>\n dplyr::select(dplyr::all_of(c(\"month\", \"pred\", \"date\"))) |>\n dplyr::group_by(month) \n\ndplyr::count(y, month) |>\n print(n = 12)\n\nSimple feature collection with 12 features and 2 fields\nGeometry type: MULTIPOINT\nDimension: XY\nBounding box: xmin: -75.7589 ymin: 35.1211 xmax: -65.48274 ymax: 43.83954\nGeodetic CRS: WGS 84\n# A tibble: 12 × 3\n month n geometry\n * <fct> <int> <MULTIPOINT [°]>\n 1 Jan 21 ((-74.63902 36.26849), (-75.01758 36.49984), (-75.01801 36.72554…\n 2 Feb 7 ((-74.52432 37.24967), (-74.45561 37.16891), (-74.74373 36.72355…\n 3 Mar 23 ((-74.53117 36.26996), (-74.60195 36.72201), (-74.67127 36.72266…\n 4 Apr 169 ((-72.924 38.6733), (-73.0165 38.591), (-73.0036 38.56), (-73.10…\n 5 May 119 ((-74.56571 35.6059), (-75.2181 35.1934), (-75.3228 35.535), (-7…\n 6 Jun 53 ((-73.10608 38.72575), (-74.86204 36.27105), (-75.04656 36.34824…\n 7 Jul 48 ((-74.53554 36.19828), (-74.91756 36.27104), (-75.10905 36.27065…\n 8 Aug 39 ((-72.78628 38.68677), (-72.98868 38.61241), (-74.9889 36.2911),…\n 9 Sep 21 ((-75.3167 36.0439), (-75.5204 36.3294), (-75.5519 36.1854), (-7…\n10 Oct 79 ((-67.06445 42.91399), (-68.43614 43.83954), (-69.14391 43.16967…\n11 Nov 19 ((-72.52681 39.21286), (-71.54966 39.99385), (-67.79606 40.36107…\n12 Dec 14 ((-75.242 35.2705), (-75.3335 35.3027), (-75.436 35.1211), (-75.…\n\n\nNow how about one ROC plot for each month? Yikes! This requires a iterative approach, using group_map(), to compute the ROC for each month. We then follow with plot wrapping by the patchwork package.\n\npaucs = dplyr::group_map(y, \n function(tbl, key, pred_rasters = NULL){\n ix = key$month %in% month.abb\n x = dplyr::slice(pred_rasters, \"time\", ix)\n pauc = maxnetic::pAUC(x, tbl)\n plot(pauc,title = key$month, \n xlab = \"\", ylab = \"\")\n }, pred_rasters = pred)\n\npatchwork::wrap_plots(paucs, ncol = 4)\n\n\n\n\nHmmm. That’s surprising, yes? Why during the summer months does our AUC go down when we have the most number of observations? That seems counter intuitive." }, { "objectID": "modeling-01.html#thinking-about-auc", @@ -165,7 +165,7 @@ "href": "covariates.html#loading-data", "title": "Covariates for observations and background", "section": "2 Loading data", - "text": "2 Loading data\nWe have two data sources to load: point observation data and rasterized environmental predicitor data.\n\n2.1 Load the observation data\nWe’ll load in our OBIS observations as a flat table, and immediately filter the data to any occurring from 2000 to present.\n\nsource(\"setup.R\")\n\nobs = read_obis(form = \"sf\") |>\n dplyr::filter(date >= as.Date(\"2000-01-01\")) |>\n dplyr::glimpse()\n\nRows: 8,561\nColumns: 8\n$ occurrenceID <chr> \"1817_15415\", \"513_6751\", \"513_49055\", \"513_49875\", \"513…\n$ date <date> 2016-08-07, 2002-06-17, 2009-07-10, 2009-08-17, 2016-06…\n$ basisOfRecord <chr> \"HumanObservation\", \"HumanObservation\", \"HumanObservatio…\n$ bathymetry <dbl> 121, 65, 166, 133, 218, 90, 1283, 160, 219, 97, 131, 97,…\n$ shoredistance <dbl> 134559, 77684, 127643, 58028, 94816, 141127, 114100, 382…\n$ sst <dbl> 16.36, 13.38, 11.70, 9.20, 11.79, 13.90, 18.34, 9.08, 11…\n$ sss <dbl> 34.10, 32.64, 32.22, 30.99, 32.12, 33.38, 34.96, 30.47, …\n$ geom <POINT [°]> POINT (-72.8074 39.056), POINT (-70.5774 40.5725),…\n\n\n\n\n2.2 Load the environmental predictors\nNext we load the environmental predictors, sst and wind (as windspeed, u_wind and v_wind). For each we first read in the database, then compose filenames, read in the rasters and organize into layers by date, then rename to a pretty-name and finally shift the rasters from their native 0-to-360 longitude range to longitude -180-to-180 which matches our observations.\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date)\n\nsst = sst_db |>\n oisster::compose_filename(path = sst_path) |>\n stars::read_stars(along = list(time = sst_db$date)) |>\n rlang::set_names(\"sst\")|>\n st_to_180()\n\n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)\n\nwindspeed_db = wind_db |>\n dplyr::filter(param == \"windspeed\")\nwindspeed = windspeed_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = windspeed_db$date)) |>\n rlang::set_names(\"windspeed\") |>\n st_to_180()\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")\nu_wind = u_wind_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = u_wind_db$date)) |>\n rlang::set_names(\"u_wind\") |>\n st_to_180()\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")\nv_wind = v_wind_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = v_wind_db$date)) |>\n rlang::set_names(\"v_wind\") |>\n st_to_180()\n\nWe’ll set these aside for a moment and come back to them after we have established our background points." + "text": "2 Loading data\nWe have two data sources to load: point observation data and rasterized environmental predictor data.\n\n2.1 Load the observation data\nWe’ll load in our OBIS observations as a flat table, and immediately filter the data to any occurring from 2000 to present.\n\nsource(\"setup.R\")\n\nobs = read_obis(form = \"sf\") |>\n dplyr::filter(date >= as.Date(\"2000-01-01\")) |>\n dplyr::glimpse()\n\nRows: 8,561\nColumns: 8\n$ occurrenceID <chr> \"1817_15415\", \"513_6751\", \"513_49055\", \"513_49875\", \"513…\n$ date <date> 2016-08-07, 2002-06-17, 2009-07-10, 2009-08-17, 2016-06…\n$ basisOfRecord <chr> \"HumanObservation\", \"HumanObservation\", \"HumanObservatio…\n$ bathymetry <dbl> 121, 65, 166, 133, 218, 90, 1283, 160, 219, 97, 131, 97,…\n$ shoredistance <dbl> 134559, 77684, 127643, 58028, 94816, 141127, 114100, 382…\n$ sst <dbl> 16.36, 13.38, 11.70, 9.20, 11.79, 13.90, 18.34, 9.08, 11…\n$ sss <dbl> 34.10, 32.64, 32.22, 30.99, 32.12, 33.38, 34.96, 30.47, …\n$ geom <POINT [°]> POINT (-72.8074 39.056), POINT (-70.5774 40.5725),…\n\n\n\n\n2.2 Load the environmental predictors\nNext we load the environmental predictors, sst and wind (as windspeed, u_wind and v_wind). For each we first read in the database, then compose filenames, read in the rasters and organize into layers by date, then rename to a pretty-name and finally shift the rasters from their native 0-to-360 longitude range to longitude -180-to-180 which matches our observations.\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date)\n\nsst = sst_db |>\n oisster::compose_filename(path = sst_path) |>\n stars::read_stars(along = list(time = sst_db$date)) |>\n rlang::set_names(\"sst\")|>\n st_to_180()\n\n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)\n\nwindspeed_db = wind_db |>\n dplyr::filter(param == \"windspeed\")\nwindspeed = windspeed_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = windspeed_db$date)) |>\n rlang::set_names(\"windspeed\") |>\n st_to_180()\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")\nu_wind = u_wind_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = u_wind_db$date)) |>\n rlang::set_names(\"u_wind\") |>\n st_to_180()\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")\nv_wind = v_wind_db |>\n nbs::compose_filename(path = wind_path) |>\n stars::read_stars(along = list(time = v_wind_db$date)) |>\n rlang::set_names(\"v_wind\") |>\n st_to_180()\n\nWe’ll set these aside for a moment and come back to them after we have established our background points." }, { "objectID": "covariates.html#sampling-background-data", diff --git a/modeling-01_files/figure-html/unnamed-chunk-11-1.png b/modeling-01_files/figure-html/unnamed-chunk-11-1.png index b605749b935855a760236faf4b2adaf32c04bce6..3c6cc2e77351bfbc5400444e4694742edd4c6d22 100644 GIT binary patch literal 258862 zcmd>mRa9Kf)-4u-OK=DfAh^4`yN85Ef;)}VI0SchcZU!h8n+<91Hl@1cZb{g&iTjq zU(P-E{XX>QJ(}88Ypq%`*W9%US5uKiMWnIMp9N1XbW`GbTBe8lQpw9bFwngkd=Ue;fs#e*0+4GjVqGbTF>}#JaD$8 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zd6A*;r1G;ViT7RX0^Q5r_Ouh=zB%sQwOX_A@3~jrXr*_t$suC!=AQwYuIp<7E8T-~MYvqA1{vt|Ll#XMWNDzGC?a0I2`Y z;{QD>|NfOpSy=8KN6FFu#Os+uXgi*y1ET+N4q%qpVV3^?o`eIDXKuv_Rjz^VXz)*3 MQbD3f{E`3v0kQ|zod5s; diff --git a/modeling.html b/modeling.html deleted file mode 100644 index eb166f6..0000000 --- a/modeling.html +++ /dev/null @@ -1,377 +0,0 @@ - - - - - - - - - -Presence-only modeling - - - - - - - - - - - - - - - - - - - -

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Presence-only modeling

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So at this point we have point data for observation and background that have been joined with common environmental covariates (aka predictors). With that data in hand, building the model is easy. To make things interesting we’ll develop models by month. We’ll follow up with some simple model assessments.

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Load data

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Here we load the observation and background data points. We add a column identifying the month of the year.

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obs = sf::read_sf(file.path("data", "obs", "obs-covariates.gpkg")) |>
-  sf::st_set_geometry("geometry") |>
-  dplyr::mutate(month = factor(format(month_id, "%b"), levels = month.abb), 
-                .before = geometry)
-bkg = sf::read_sf(file.path("data", "bkg", "bkg-covariates.gpkg")) |>
-  sf::st_set_geometry("geometry") |>
-  dplyr::mutate(month = factor(format(month_id, "%b"), levels = month.abb), 
-                .before = geometry)
-
-

Let’s do a quick check by counting each by month.

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sf::st_drop_geometry(obs) |> 
-  dplyr::count(month, name = "n_obs")|>
-  dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = "n_bkg"), 
-                   by = 'month') |>
-  print(n = 12)
-
-
# A tibble: 12 × 3
-   month n_obs n_bkg
-   <fct> <int> <int>
- 1 Jan      33    55
- 2 Feb      40    55
- 3 Mar      50   106
- 4 Apr     341   553
- 5 May     541   934
- 6 Jun    2137  3358
- 7 Jul    2108  3225
- 8 Aug    1698  2627
- 9 Sep     725  1135
-10 Oct     328   507
-11 Nov     494   760
-12 Dec      66   103
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