---
title: "Documenting and auditing the weights: the quality report"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Documenting and auditing the weights: the quality report}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(weightflow)
has_rpart <- requireNamespace("rpart", quietly = TRUE)
```

`report_weighting()` turns a prepped recipe into a single, self-contained HTML
report that documents how the weights were built and how they behave. It follows
the logic of an official-statistics quality report (GSBPM sub-process 5.6 and the
ESS SIMS/ESMS metadata concepts) and needs no external dependency: every plot is
inline SVG, so the file opens anywhere and can be archived or emailed as is.

## A worked recipe

To show a report with something to say, we run a full cascade on the bundled
`sample_survey` (one row per person, with households, so the weights can be kept
equal within household): an unknown-eligibility redistribution, a nonresponse
adjustment with a regression tree estimated by k-fold cross-fitting, a linear calibration to region
(constant within a household) that gives every member of a household one weight, a trimming that
preserves those calibration totals (also one factor per household), and a
totals-preserving rounding. We add a small bootstrap so the report can also
document the variance design.

```{r fit, eval = has_rpart, warning = FALSE, message = FALSE}
totals_r <- colSums(model.matrix(~ region, population))

spec <- weighting_spec(sample_survey, base_weights = pw) |>
  step_unknown_eligibility(unknown = unknown_elig, by = "region",
                           cluster = "household_id") |>
  step_nonresponse(respondent = responded, method = "propensity",
                   engine = "tree", formula = ~ region + sex + age,
                   crossfit = 5, crossfit_seed = 1, num_classes = NULL,
                   cluster = "household_id") |>
  step_calibrate(method = "linear", formula = ~ region, totals = totals_r,
                 cluster = "household_id", equal_within_cluster = TRUE)

# feasible trim interval from the calibrated weights
w  <- collect_weights(prep(spec), drop_zero = FALSE)$.weight
w  <- w[w > 0]
lo <- as.numeric(quantile(w, 0.02)); up <- as.numeric(quantile(w, 0.98))

recipe <- spec |>
  step_trim_calibrated(~ region, lower = lo, upper = up,
                       cluster = "household_id", equal_within_cluster = TRUE) |>
  step_round(method = "preserve_total")

fit  <- prep(recipe)
boot <- bootstrap_weights(recipe, replicates = 15, strata = "region",
                          psu = "psu", seed = 1, progress = FALSE)

path <- report_weighting(
  fit, replicates = boot, domains = ~ region, open = FALSE,
  metadata = list(
    survey           = "Living Conditions Survey (illustrative)",
    reference_period = "2024",
    geography        = "National",
    producer         = "National Statistical Office",
    frame            = "Master sample of dwellings",
    totals_source    = "Population projections (2023)",
    totals_date      = "2024-06-30"))
```

The generated report is embedded below (open `path` in a browser for a
full-window view). If the `rpart` package is not installed the tree engine cannot
run and the report is not shown here; the description that follows still applies.

```{r embed, echo = FALSE, results = "asis", eval = has_rpart}
h <- paste(readLines(path, warn = FALSE), collapse = "\n")
h <- gsub("&", "&amp;", h, fixed = TRUE)
h <- gsub('"', "&quot;", h, fixed = TRUE)
cat(sprintf(
  '<iframe srcdoc="%s" width="100%%" height="720" style="border:1px solid #ddd;border-radius:8px"></iframe>',
  h))
```

## What the report contains

The report is a sequence of cards, in reading order.

Executive summary. A prose summary of the cascade with the headline design
effect, effective sample size and R-indicator, a plain-language interpretation of
the final design effect, and a status checklist (convergence, extreme weights,
replicate weights, alerts). Anything that needs attention (for example a step
that did not converge, or a quality alert) is collected here.

Reference metadata. When `metadata` is supplied, a header card aligned to the
SIMS/ESMS concepts. Recognised keys get a proper label; any other key is shown as
given.

Fieldwork outcomes (AAPOR). Because the recipe has eligibility and nonresponse
steps, a card reconstructs the disposition of every case (ineligible, unknown
eligibility, eligible respondent, eligible nonrespondent) and reports the
eligibility rate together with the response rate in three AAPOR variants, from
the most to the least conservative treatment of the unknown-eligibility cases
(RR1, RR3/CASRO, RR5), both unweighted and base-weighted.

Per-stage summary. A table with the active units, sum of weights, CV, Kish
design effect and effective sample size at each stage; an inline chart of the
design effect across stages; and a per-step impact table (the change in deff and
CV at each step and its share of the total change).

Domain reliability. Because `domains = ~ region` was supplied, a card shows the
active n, CV, design effect and effective sample size within each region, so
thin domains (small effective n) are visible.

Replication design. Because replicate weights were passed through `replicates`,
a card documents the variance design: method, number of replicates, strata and
PSUs per stratum, lonely-PSU handling, cores and run time.

Weight distribution and per-step detail. The distribution of the final weights,
and a collapsible section with one card per step: the requested parameters, the
diagnostics (for calibration, the target and achieved totals with their relative
difference), and a before/after scatter of the weights.

## Arguments

Set `open = TRUE` to launch the report in a browser, or pass `file` to choose the
path. `lang = "es"` produces the narrative in Spanish. `narrative = FALSE` gives
the previous tables-only report, and `plots = FALSE` drops the per-step visuals.

The variance card documents the design of the replication. The standard errors,
CVs and confidence intervals of specific estimates are computed downstream with
the `survey` or `srvyr` package (see the Variance estimation article).
