Load demo 2 analysis settings from YAML

analysis/legacy-params.R

@@ -89,22 +89,30 @@ legacy_indicator_symbols <- c(
   prepare_raw = "prepare.raw", convert_percent = "convert.percent", logit = "logit",
   remove_zero_cover_plots = "remove.zero.cover.plots", scale_abundance = "scale.abund",
   td_multiplier = "td.multiplier", td_multiplier_omit = "td.multiplier.omit",
-  exclude_exotics = "exclude.exotics", na_to_zero = "NA.TO.ZERO"
+  exclude_exotics = "exclude.exotics", na_to_zero = "NA.TO.ZERO",
+  multiplier = "ei.multiplier", normalize_area_m2 = "normalize.m2"
 )
 
 legacy_project_params <- function(config) {
   values <- config[names(legacy_project_symbols)]
   names(values) <- unname(legacy_project_symbols)
-  active <- unlist(config$active_indicators, use.names = FALSE)
+  active <- as.character(unlist(config$active_indicators, use.names = FALSE))
   if (config$td_only) active <- "TD"
   values$EI.vec <- active
   values$EI.F.GROUPS <- cbind(
     EI = active,
-    f.group = unname(unlist(config$indicator_groups[active]))
+    f.group = as.character(unlist(config$indicator_groups[active], use.names = FALSE))
   )
   values$df.EI.FG <- as.data.frame(values$EI.F.GROUPS)
   values$EI.list <- data.frame(fname = active)
-  values$EI.agg.list <- data.frame(fname = unlist(config$aggregate_indicators, use.names = FALSE))
+  values$EI.agg.list <- data.frame(
+    fname = as.character(
+      unlist(
+        config$aggregate_indicators,
+        use.names = FALSE
+      )
+    )
+  )
   nodata <- unlist(config$vegetation_without_data, use.names = FALSE)
   values$veg.nodata <- if (length(nodata)) nodata else ""
   values$ei.params <- function(ei_code) {
@@ -117,10 +125,17 @@ legacy_project_params <- function(config) {
     if (is.null(parameters$td.multiplier)) parameters$td.multiplier <- NA
     omit <- unlist(parameters$td.multiplier.omit, use.names = FALSE)
     parameters$td.multiplier.omit <- if (length(omit)) omit else ""
-    parameters$ei.data.type <- if (ei_code %in% c("SR", "TD")) config$data_type else "cover"
+    parameters$ei.data.type <- if (!is.null(settings$data_type)) {
+      settings$data_type
+    } else if (ei_code %in% c("SR", "TD", "TS")) {
+      config$data_type
+    } else {
+      "cover"
+    }
     inputs <- c(
-      SR = "speciesCover", TD = "speciesCover", PCGF = "coverByGrowthForm",
-      GC = "groundCover", PCESS = "exoticCoverByStratum"
+      SR = "speciesCover", TD = "speciesCover", TS = "speciesCover", PCGF = "coverByGrowthForm",
+      GC = "groundCover", PCESS = "exoticCoverByStratum", PCES = "exoticCoverByStratum",
+      BA = "speciesStems", ASC = "speciesStems"
     )
     parameters$df.input <- if (parameters$ei.data.type == "ind") {
       "speciesStems"
@@ -139,5 +154,19 @@ legacy_project_params <- function(config) {
     names(parameters) <- unname(symbols)
     c(list(land.cover = land_cover), parameters)
   }
+  if (!is.null(config$stem_sampling)) {
+    values$dbh.ba.scaled <- function(dbh, target_area_m2 = NULL) {
+      sampling <- config$stem_sampling
+      if (is.null(target_area_m2)) target_area_m2 <- 10000
+      dplyr::case_when(
+        dbh >= sampling$min_dbh_cm & dbh < sampling$split_dbh_cm ~
+          pi * ((dbh / 2)^2) / 10000 * target_area_m2 / sampling$small_plot_area_m2,
+        dbh >= sampling$split_dbh_cm ~
+          pi * ((dbh / 2)^2) / 10000 * target_area_m2 / sampling$large_plot_area_m2,
+        is.na(dbh) ~ NA,
+        .default = NA
+      )
+    }
+  }
   values
 }

analysis/legacy.R

@@ -40,7 +40,7 @@ prepare_legacy_environment <- function(context = NULL, config = list(), seed = N
   run_env$VQA_COMMON_PARAMS <- legacy_common_params(
     if (is.null(context)) load_default_params() else config
   )
-  if (!is.null(context) && identical(project, "vqa-demo1")) {
+  if (!is.null(context) && project %in% c("vqa-demo1", "vqa-demo2")) {
     run_env$VQA_PROJECT_PARAMS <- legacy_project_params(config)
   }
   sys.source("params.R", envir = run_env, toplevel.env = run_env)

data/vqa-demo1-min/params.yaml

@@ -18,6 +18,4 @@ active_indicators:
 - PCGF
 - GC
 - PCESS
-indicator_defaults:
-  scale_abundance: yes
 source_plot_data_filename: vqa-demo1_data.accdb

data/vqa-demo2-min/params.yaml

@@ -1,21 +1,31 @@
 project: vqa-demo2-min
-
-# Validation-only manifest for now.
-# This is a declarative subset of params/params.vqa-demo2.R.
 data_type: mixed
-qh_method: empirical
-td_only: false
-include_plots_nodata: true
-
-active_indicators:
-  - SR
-  - TS
-  - PCES
-  - PCGF
-  - BA
-  - ASC
-
+bootstrap_reps: 10
+min_plots: 4
+omit_quality_hectares: false
+functional_group_group_by: indicator_group
+functional_group_method: generalized_mean
+overall_quality_method: mixed
+nodata_sd: 4
+aggregate_indicators: [PCGF, ASC]
+active_indicators: [SR, TS, PCES, PCGF, BA, ASC]
+source_plot_data_filename: Demo_data_main.xlsx
+indicator_settings:
+  SR:
+    data_type: cover
+    test_tail: both
+  ASC:
+    distribution: gamma
+stem_sampling:
+  min_dbh_cm: 2.5
+  split_dbh_cm: 10
+  small_plot_area_m2: 48
+  large_plot_area_m2: 192
 assessments:
   offset_baseline:
     active_indicators: []
+    aggregate_indicators: []
     qh_method: assume.0
+    source_plot_data_filename: Demo_data_offsets.xlsx
+  offset_current:
+    source_plot_data_filename: Demo_data_offsets.xlsx

docs/common-config-inventory.md

@@ -76,7 +76,7 @@ counts as a consumer because it decides which calculations or outputs run.
 
 ## Project analysis configuration
 
-Demo 1 uses root defaults plus `data/vqa-demo1-min/params.yaml` for both assessments.
+Both demo projects use root defaults plus their project `params.yaml` files.
 `legacy_project_symbols` and `legacy_indicator_symbols` in `analysis/legacy-params.R`
 map resolved settings to script parameters. The adapter derives indicator lists, groups,
 input filenames, and the `ei.params()` and `nmds.params()` functions from that configuration.
@@ -89,23 +89,36 @@ input filenames, and the `ei.params()` and `nmds.params()` functions from that c
 | `figure_type`, `plot_legends`, `legend_no_focal_benchmark`, `show_sample_sizes`, `legend_combined`, `plot_dists_fitted_grouped_rescaled`, `plot_dists_fitted_rescaled` | Distribution figures. |
 | `indicator_defaults`, `indicator_settings`, `nmds`, `pcess_herbs_only`, `vegetation_without_data` | Indicator scripts, NMDS fitting, and workbook indicator metadata. |
 | `min_plots`, `legacy_summary_area_ha`, `source_plot_data_filename`, `missing_exotic_status_assume_native` | Workbook metadata and preprocessing summaries. |
+| `stem_sampling` and indicator `normalize_area_m2` | Basal-area normalization using the sampled area for each DBH size class. |
 
 Both demo 1 assessments share the same settings. Their input tables determine the
 assessment differences. Root defaults contain application settings and indicator
 definitions; project YAML supplies data type, bootstrap count, aggregation choices,
-aggregate indicators, herbs-only exotic cover, and abundance scaling.
+aggregate indicators, and herbs-only exotic cover. Abundance scaling is a shared default,
+with a separate setting for taxonomic distance.
 
-Demo 2 analysis settings are supplied by `params/params.vqa-demo2.R`. Standalone commands
-such as `import.R` and `qh.net.R` load project parameter scripts through `params.R`.
-Callable demo 1 analysis supplies resolved project settings directly to that wrapper.
+Demo 2 uses mixed data: species richness selects cover observations, taxonomic similarity
+uses the project data type, and basal area and abundance by size class select stem records.
+Basal area uses configured DBH sampling geometry; abundance by size class counts individuals
+from standardized stem records. Indicator settings select the distributions, transformations,
+and handling of missing observations.
+
+The `offset_baseline` override explicitly selects `assume.0`, with no active or aggregate
+indicators. Other assessments use empirical quality. Source-data labels for the offset
+assessments are explicit overrides. Analysis settings do not depend on assessment-name
+patterns. Baseline/offset relationships for net-quality calculations are not read by the
+assessment analysis workflow.
+
+Standalone commands such as `import.R` and `qh.net.R` load project parameter scripts through
+`params.R`. Callable analysis supplies resolved project settings directly to that wrapper.
 
 ## Runtime and file conventions
 
 Runtime arguments select the data root, assessment, and seed. Analysis operations reset
 RNG seeds individually; NMDS has its own seed settings. Paths, input and output filenames,
 and indicator lists are constructed in code. `params.R` defines application file conventions.
 
-Raw filenames, import filters, include-file replacement, taxonomy/TNRS settings, and
+Raw-data loading, import filters, include-file replacement, taxonomy/TNRS settings, and
 net-quality calculations are separate from assessment analysis. Parameters such as
 `allow.discrete`, `plot.mean`, `remove.grid`, and `VQA.XLS.NO.AREA` have no reads in the
 active scripts and are retained for standalone caller compatibility.

params.yaml

@@ -79,11 +79,13 @@ indicator_defaults:
   convert_percent: no
   logit: no
   remove_zero_cover_plots: no
-  scale_abundance: no
+  scale_abundance: yes
   td_multiplier: ~
   td_multiplier_omit: []
   exclude_exotics: no
   na_to_zero: yes
+  multiplier: 1
+  normalize_area_m2: false
 indicator_settings:
   SR:
     name: Species Richness
@@ -119,12 +121,45 @@ indicator_settings:
     has_stratum: yes
     test_tail: upper
     benchmark_value: 0
+  TS:
+    name: Taxonomic Similarity
+    distribution: Bet
+    quality_method: empirical
+    has_stratum: no
+    test_tail: lower
+  PCES:
+    name: Percent Cover Exotic Species
+    distribution: Bet
+    quality_method: fixed
+    has_stratum: no
+    test_tail: upper
+    benchmark_value: 0
+  BA:
+    name: Basal Area (m2/ha)
+    distribution: gamma
+    quality_method: empirical
+    has_stratum: no
+    test_tail: both
+    data_type: ind
+    normalize_area_m2: 10000
+  ASC:
+    name: Abundance by Size Class
+    distribution: NBin
+    quality_method: empirical
+    has_stratum: yes
+    test_tail: both
+    data_type: ind
+    na_to_zero: no
 indicator_groups:
   SR: composition
   TD: composition
   PCGF: structure
   GC: function
   PCESS: integrity
+  TS: composition
+  PCES: integrity
+  BA: structure
+  ASC: structure
 nmds:
   verbose: no
   seed_enabled: yes
@@ -133,3 +168,4 @@ nmds:
   dimensions: 3
   max_iterations: 200
 missing_exotic_status_assume_native: true
+pcess_herbs_only: false

pipeline/params.R

@@ -35,7 +35,7 @@ validate_params_schema <- function(params) {
 
 validate_analysis_params <- function(params) {
   validate_params_schema(params)
-  if (isTRUE(params$project %in% c("vqa-demo1", "vqa-demo1-min"))) {
+  if (isTRUE(params$project %in% c("vqa-demo1", "vqa-demo1-min", "vqa-demo2", "vqa-demo2-min"))) {
     active <- unlist(params$active_indicators, use.names = FALSE)
     if ((identical(params$qh_method, "empirical") && !length(active)) ||
       !all(active %in% names(params$indicator_settings)) ||
@@ -58,6 +58,13 @@ validate_analysis_params <- function(params) {
           call. = FALSE
         )
       }
+      if (code == "BA" && !isFALSE(settings$normalize_area_m2) && is.null(params$stem_sampling)) {
+        stop("Basal-area normalization requires stem sampling geometry.", call. = FALSE)
+      }
+    }
+    if (!is.null(params$stem_sampling) &&
+      params$stem_sampling$min_dbh_cm >= params$stem_sampling$split_dbh_cm) {
+      stop("The stem diameter split must exceed the minimum measured diameter.", call. = FALSE)
     }
   }
   invisible(TRUE)