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modeling-02_files/execute-results/html.json

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+    "markdown": "---\ntitle: \"Modeling each month\"\ncache: true\n---\n\n\nHere we modify our first modeling workflow to produce a model for each month. In the [previous workflow](modeling-01.qmd) we produced one model covering observations covering all times; we then applied the model to the various months. \n\n## Load data\n\nHere we load the observation and background data points. We add a column identifying the month of the year. \n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-1_e35d59038fe3799ba4e644ab0d67b2d0'}\n\n```{.r .cell-code}\nsource(\"setup.R\")\nobs = sf::read_sf(file.path(\"data\", \"obs\", \"obs-covariates.gpkg\")) |>\n  sf::st_set_geometry(\"geometry\") |>\n  dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb), \n                .before = geometry)\nbkg = sf::read_sf(file.path(\"data\", \"bkg\", \"bkg-covariates.gpkg\")) |>\n  sf::st_set_geometry(\"geometry\") |>\n  dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb), \n                .before = geometry)\n```\n:::\n\n\n## Do we model every month?\n\nLet's do a quick check by counting each by month. Note that we drop the spatial info so that we can make simply tallies.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-2_80a187bb1c38cbffb49e1feee10e7252'}\n\n```{.r .cell-code}\ncounts = sf::st_drop_geometry(obs) |> \n  dplyr::count(month, name = \"n_obs\") |>\n  dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = \"n_bkg\"), \n                   by = 'month') |>\n  print(n = 12)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 12 × 3\n   month n_obs n_bkg\n   <fct> <int> <int>\n 1 Jan      33    51\n 2 Feb      40    57\n 3 Mar      50    79\n 4 Apr     341   528\n 5 May     541   943\n 6 Jun    2137  3471\n 7 Jul    2108  3233\n 8 Aug    1698  2597\n 9 Sep     725  1205\n10 Oct     328   485\n11 Nov     494   739\n12 Dec      66    90\n```\n:::\n:::\n\n\nSo the colder months have fewer observations than the warmer months.  We already knew that, but it will be interesting to see how that manifests itself in the models.\n\n### Build the monthly models\n\nSince we are building 12 models (rather than one) it is useful to create a function that computes a model for any month, and then iterate through the months of the year.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-3_14408e426c7c9e84885d104d1381d64b'}\n\n```{.r .cell-code}\n# A function for making one month's model\n#\n# @param tbl a data frame of one month's observations\n# @param key a data frame that holds the current iteration's month name\n# @param bkg a complete data frame of background data (which we filter for the given month)\n# @param path the path where the model is saved\n# @return a model, which is also saved in \"data/model/v2/v2.<monthname>\"\nmodel_month = function(tbl, key, bkg = NULL, path = \".\"){\n  \n  bkg = bkg |>\n    dplyr::filter(month == key$month) |>\n    sf::st_drop_geometry() |>\n    dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n    na.omit()\n  \n  obs = tbl |>\n    sf::st_drop_geometry() |>\n    dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n    na.omit()\n  \n  # these are the predictor variables row bound\n  x = dplyr::bind_rows(obs, bkg)\n  \n  # and the flag indicating presence/background\n  flag = c(rep(1, nrow(obs)), rep(0, nrow(bkg)))\n  \n  model_path = file.path(path, paste0(\"v2.\", key$month, \".rds\"))\n\n  model = maxnet::maxnet(flag, x) |>\n    maxnetic::write_maxnet(model_path)\n                         \n  model\n}\n\npath = file.path(\"data\", \"model\", \"v2\")\nok = dir.create(path, recursive = TRUE, showWarnings = FALSE)\nmodels = obs |>\n  dplyr::group_by(month) |>\n  dplyr::group_map(model_month, bkg = bkg, path = path) |>\n  rlang::set_names(levels(obs$month))\n```\n:::\n\n\nWe can look at the response plots for every month, but for demonstration purposes, we'll just show one month.  It is interesting to compare this respinse to that for the [basic model](modeling-01.qmd).\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-4_56127f4832cd034c70d9efb8b0100319'}\n\n```{.r .cell-code}\nplot(models[['Jun']], type = 'cloglog')\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n:::\n\n\n## Predict with rasters\nFirst we load the raster databases as these are lightweight to pass into a function that iterates through the months.\n\n### Load the raster databases (`sst` and `u_wind` and `v_wind`)\n\nWe also make sure they are in date order and add a \"month\" variable to each.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-5_aec22c8734e04228e9c12ddd7dbebd94'}\n\n```{.r .cell-code}\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n  dplyr::arrange(date) |>\n  dplyr::mutate(month = format(date, \"%b\"))\n  \n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n  dplyr::arrange(date)|>\n  dplyr::mutate(month = format(date, \"%b\"))\n\nu_wind_db = wind_db |>\n  dplyr::filter(param == \"u_wind\")\n\nv_wind_db = wind_db |>\n  dplyr::filter(param == \"v_wind\")\n```\n:::\n\n\n### Iterate through the months making predictions\nNow we can build an iterator function that will make a prediction for each month.  Let's narrow our predictions to just those for a particular year, 2019, and read the rasters in all at once.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-6_c05a1ac68684d54c6ff8b072d57654ad'}\n\n```{.r .cell-code}\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\nx = read_predictors(\n  sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])),\n  u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])),\n  v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2]))\n)\n```\n:::\n\n\nNow we can iterate through the months.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-7_88b6e121185be4feda4a2f5f5d3eb399'}\n\n```{.r .cell-code}\ndate_sequence = seq(from = dates[1], to = dates[2], by = \"month\")\npred_rasters = lapply(names(models),\n  function(mon){\n    ix = which(month.abb %in% mon)\n    predict(models[[mon]], dplyr::slice(x, time, ix, drop), type = \"cloglog\")\n  }) \npred_rasters = do.call(c, append(pred_rasters, list(along = list(time = date_sequence))))\n```\n:::\n\n\nLet's plot them.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-8_004777e1101dedbce37699ae5d54f4c2'}\n\n```{.r .cell-code}\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n  sf::st_geometry() |>\n  sf::st_crop(pred_rasters)\n\nplot_coast = function() {\n  plot(coast, col = 'green', add = TRUE)\n}\nplot(pred_rasters, hook = plot_coast)\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\nLet's see what we can discern from the predict abilities. We can extract the predicted values at the observed locations.  Having those in hand allows us to compute pAUC for each month.\n\n\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-9_a7e317262834717357a0fd9d3b7b9f20'}\n\n```{.r .cell-code}\npred_obs = stars::st_extract(pred_rasters, \n                             dplyr::filter(obs, dplyr::between(date, dates[1], dates[2])),\n                             time_column = \"month_id\") |>\n  dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb)) |>\n  dplyr::group_by(month)\n\npaucs = dplyr::group_map(pred_obs,\n                        function(x, y) {\n                          ix = month.abb %in% y$month\n                          s = dplyr::slice(pred_rasters, \"time\", ix)\n                          pauc = maxnetic::pAUC(s,x)\n                          dplyr::tibble(month = y$month, \n                                        auc = pauc$area,\n                                        pauc = list(pauc))\n                        })|>\n  dplyr::bind_rows() |>\n  print(n = 12)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 12 × 3\n   month   auc pauc      \n   <fct> <dbl> <list>    \n 1 Jan   0.703 <pAUC [3]>\n 2 Feb   0.689 <pAUC [3]>\n 3 Mar   0.698 <pAUC [3]>\n 4 Apr   0.677 <pAUC [3]>\n 5 May   0.654 <pAUC [3]>\n 6 Jun   0.662 <pAUC [3]>\n 7 Jul   0.665 <pAUC [3]>\n 8 Aug   0.696 <pAUC [3]>\n 9 Sep   0.663 <pAUC [3]>\n10 Oct   0.633 <pAUC [3]>\n11 Nov   0.627 <pAUC [3]>\n12 Dec   0.665 <pAUC [3]>\n```\n:::\n:::\n\n\nNote that last element, `pauc`, is the result returned by the `maxnetic::pAUC()` function which we can plot.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-10_d4439b1d52a25443e93dea2dfb9413f1'}\n\n```{.r .cell-code}\npp = paucs |>\n  dplyr::group_by(month) |>\n  dplyr::group_map(\n    function(tbl, key){\n      plot(tbl$pauc[[1]], title = key$month, xlab = \"\", ylab = \"\")\n    }\n  )\npatchwork::wrap_plots(pp, ncol = 4)\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\nWell, it would be easy to become dispirited by this result.  It would be reasonable to expect AUC values to improve if we built monthly models rather than a single model applied to any month.  But it seems to not be the dramatic improvement hoped for. Darn!",
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