The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Validation against the survey package

weightflow’s calibration is meant to reproduce the established results of the survey package on every method they share, while adding the staged cascade (eligibility, nonresponse, selection) and a recipe-aware bootstrap on top. This vignette checks that agreement directly and visually: on the same starting weights and the same control totals, the two packages return the same weights for

Each method is shown with a scatter of the g-weights (adjustment factors, g = final / base) of the two packages against the y = x line; we also plot the distribution of those adjustment factors and close with a table of estimates. To make every unit comparable one-to-one, the recipes below use only the calibration step (no dropping or nonresponse), so no rows are removed.

d   <- sample_survey
pop <- population

# a demographic breakdown gives the calibration more auxiliary variables
brk <- c(0, 30, 45, 60, Inf); lab <- c("18-30", "31-45", "46-60", "60+")
d$age_grp   <- cut(d$age,   brk, labels = lab)
pop$age_grp <- cut(pop$age, brk, labels = lab)

# tidy population margins reused throughout
reg_tab <- as.data.frame(table(region  = pop$region))
sex_tab <- as.data.frame(table(sex     = pop$sex))
age_tab <- as.data.frame(table(age_grp = pop$age_grp))

# model-matrix totals for the region + sex + age-group calibration
totals  <- colSums(model.matrix(~ region + sex + age_grp, pop))
# weightflow brand palette (from the pkgdown site)
wf_primary <- "#3d3580"; wf_violet <- "#7a6ad0"
wf_green   <- "#1d9e75"; wf_amber  <- "#c9822b"; wf_grey <- "#6b7280"

theme_wf <- function() {
  ggplot2::theme_minimal(base_size = 11) +
    ggplot2::theme(
      plot.title    = ggplot2::element_text(face = "bold", colour = wf_primary),
      plot.subtitle = ggplot2::element_text(colour = wf_grey, size = 9),
      axis.title    = ggplot2::element_text(colour = wf_grey),
      legend.position = "top",
      panel.grid.minor = ggplot2::element_blank())
}

# Helper: numeric agreement + the OVERLAP of the two g-weight (adjustment factor,
# g = final / base) distributions. Because the two packages agree, the survey and
# weightflow densities land exactly on top of each other.
agree <- numeric(0)                                   # collect max|diff| per method

compare <- function(w_sv, w_wf, label, base = d$pw) {
  diff <- max(abs(w_wf - w_sv))
  agree[[label]] <<- diff                             # store for the final table
  if (has_ggplot) {
    g <- rbind(data.frame(package = "survey",     g = w_sv / base),
               data.frame(package = "weightflow", g = w_wf / base))
    print(ggplot2::ggplot(g, ggplot2::aes(g, fill = package, colour = package)) +
      ggplot2::geom_density(alpha = 0.4, linewidth = 0.5) +
      ggplot2::scale_fill_manual(values = c(survey = wf_amber, weightflow = wf_violet)) +
      ggplot2::scale_colour_manual(values = c(survey = wf_amber, weightflow = wf_violet)) +
      ggplot2::labs(title = label,
                    subtitle = sprintf("g-weights overlap (survey vs weightflow) · max |w_wf - w_sv| = %.1e", diff),
                    x = "g (adjustment factor)", y = "density", fill = NULL, colour = NULL) +
      theme_wf())
  }
  invisible(diff)
}

Post-stratification

Post-stratifying to the joint population counts of region × sex.

library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#> 
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#> 
#>     dotchart
ps_tab <- as.data.frame(table(region = pop$region, sex = pop$sex))

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "poststratify", totals = ps_tab, count = "Freq") |>
  prep()
w_wf <- wf$final_weight

des    <- svydesign(ids = ~1, weights = ~pw, data = d)
des_ps <- postStratify(des, ~region + sex, ps_tab)
w_sv   <- weights(des_ps)

compare(w_sv, w_wf, "Post-stratification (region x sex)")

Raking

Raking (iterative proportional fitting) to the region, sex and age_grp margins. We tighten survey’s convergence so both solve to the same precision.

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "raking", totals = list(reg_tab, sex_tab, age_tab),
                 count = "Freq") |>
  prep()
w_wf <- wf$final_weight

des_rk <- rake(des, list(~region, ~sex, ~age_grp), list(reg_tab, sex_tab, age_tab),
               control = list(epsilon = 1e-10, maxit = 100))
w_sv   <- weights(des_rk)

compare(w_sv, w_wf, "Raking (region + sex + age group)")

Calibration distances

Linear/GREG calibration to the totals of ~ region + sex + age_grp can be solved with any of the three distance functions. weightflow’s calfun maps one-to-one onto survey’s calfun. We keep the g-weights from each to compare their distributions afterwards.

Linear (chi-square) distance – the GREG estimator

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ region + sex + age_grp,
                 totals = totals, calfun = "linear") |>
  prep()
w_wf <- wf$final_weight
w_sv <- weights(calibrate(des, ~ region + sex + age_grp, population = totals,
                          calfun = "linear"))
g_linear <- w_wf / d$pw
compare(w_sv, w_wf, "Distance: linear (GREG)")

Raking (exponential) distance – always-positive weights

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ region + sex + age_grp,
                 totals = totals, calfun = "raking", maxit = 500, tol = 1e-10) |>
  prep()
w_wf <- wf$final_weight
w_sv <- weights(calibrate(des, ~ region + sex + age_grp, population = totals,
                          calfun = "raking", maxit = 500, epsilon = 1e-10))
g_raking <- w_wf / d$pw
compare(w_sv, w_wf, "Distance: raking (exponential)")

Logit distance – bounded g-weights

bnds <- c(0.5, 2)
wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ region + sex + age_grp,
                 totals = totals, calfun = "logit", bounds = bnds,
                 maxit = 500, tol = 1e-10) |>
  prep()
w_wf <- wf$final_weight
w_sv <- weights(calibrate(des, ~ region + sex + age_grp, population = totals,
                          calfun = "logit", bounds = bnds, maxit = 500, epsilon = 1e-10))
g_logit <- w_wf / d$pw
compare(w_sv, w_wf, "Distance: logit (bounded)")

Distribution of the adjustment factors

The distance function shapes how far the weights move. The linear distance is symmetric (and can dip below the dashed line at g = 1), raking keeps every weight positive, and logit is bounded by construction.

gdist <- rbind(
  data.frame(distance = "linear", g = g_linear),
  data.frame(distance = "raking", g = g_raking),
  data.frame(distance = "logit",  g = g_logit))
gdist$distance <- factor(gdist$distance, levels = c("linear", "raking", "logit"))

ggplot2::ggplot(gdist, ggplot2::aes(g, fill = distance)) +
  ggplot2::geom_density(alpha = 0.4, colour = NA) +
  ggplot2::geom_vline(xintercept = 1, linetype = "dashed", colour = wf_grey) +
  ggplot2::scale_fill_manual(values = c(linear = wf_primary, raking = wf_green,
                                        logit = wf_amber)) +
  ggplot2::labs(title = "Distribution of adjustment factors",
                subtitle = "g = final / base, by calibration distance",
                x = "g (adjustment factor)", y = "density", fill = NULL) +
  theme_wf()

Integrative calibration (one weight per household)

When every person in a household must carry the same weight, weightflow uses the Lemaitre-Dufour (1987) integrative method (cluster = "household_id", equal_within_cluster = TRUE). survey reaches the same result through aggregate.stage (Vanderhoeft 2001). The base weight pw is constant within household here, so the constraint is well defined.

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ region + sex + age_grp,
                 totals = totals, cluster = "household_id",
                 equal_within_cluster = TRUE) |>
  prep()
w_wf <- wf$final_weight

des_hh  <- svydesign(ids = ~household_id, weights = ~pw, data = d)
des_int <- calibrate(des_hh, ~ region + sex + age_grp, population = totals,
                     calfun = "linear", aggregate.stage = 1)
w_sv    <- weights(des_int)

within_hh <- max(tapply(w_wf, d$household_id, function(z) diff(range(z))))
cat("max within-household weight range (weightflow):", within_hh, "\n")
#> max within-household weight range (weightflow): 0

compare(w_sv, w_wf, "Integrative (one weight per household)")

Domain (partitioned) calibration

weightflow can calibrate each domain independently to its own totals in a single call with by =. Here each region is calibrated to its own sex and age_grp distributions; survey reaches the same weights by calibrating one region at a time.

sex_by_region <- as.data.frame(table(region = pop$region, sex     = pop$sex))
age_by_region <- as.data.frame(table(region = pop$region, age_grp = pop$age_grp))

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ sex + age_grp,
                 totals = list(sex = sex_by_region, age_grp = age_by_region),
                 count = "Freq", by = "region") |>
  prep()
w_wf <- wf$final_weight

w_sv <- numeric(nrow(d))
for (r in levels(d$region)) {
  idx   <- which(d$region == r)
  des_r <- svydesign(ids = ~1, weights = ~pw, data = d[idx, ])
  tot_r <- colSums(model.matrix(~ sex + age_grp, pop[pop$region == r, ]))
  w_sv[idx] <- weights(calibrate(des_r, ~ sex + age_grp, population = tot_r,
                                 calfun = "linear"))
}

compare(w_sv, w_wf, "Domain calibration (by region)")

Estimates agree too

Because the weights coincide, so do the estimates. Using the raking-calibrated weights, we estimate a survey outcome (respondent age) both ways and compare in a single table:

wf <- weighting_spec(d, base_weights = pw) |>
  step_calibrate(method = "raking", totals = list(reg_tab, sex_tab, age_tab),
                 count = "Freq") |>
  prep()
w_wf   <- wf$final_weight
des_rk <- rake(des, list(~region, ~sex, ~age_grp), list(reg_tab, sex_tab, age_tab),
               control = list(epsilon = 1e-10, maxit = 100))

est <- data.frame(
  quantity   = c("mean(age)", "total(age)"),
  weightflow = c(weighted.mean(d$age, w_wf), sum(w_wf * d$age)),
  survey     = c(as.numeric(coef(svymean(~age, des_rk))),
                 as.numeric(coef(svytotal(~age, des_rk)))))
est$difference <- est$weightflow - est$survey
est
#>     quantity   weightflow       survey    difference
#> 1  mean(age)     42.38993     42.38993 -4.132232e-10
#> 2 total(age) 190542.74882 190542.74882 -1.856970e-06

The design-based standard errors (from survey, or from weightflow’s recipe-aware bootstrap) are the subject of the Variance estimation article; here the point is that the calibrated point estimates are the same.

Agreement summary

Across every method and distance, weightflow reproduces survey to numerical tolerance. The closed-form methods (post-stratification, linear) agree to machine precision; the iterative ones (raking, logit, integrative) agree to the shared convergence tolerance.

data.frame(method = names(agree), `max abs weight difference` = unname(agree),
           check.names = FALSE, row.names = NULL)
#>                                   method max abs weight difference
#> 1     Post-stratification (region x sex)              1.776357e-15
#> 2      Raking (region + sex + age group)              7.927343e-08
#> 3                Distance: linear (GREG)              3.552714e-15
#> 4         Distance: raking (exponential)              9.016560e-08
#> 5              Distance: logit (bounded)              2.999771e-07
#> 6 Integrative (one weight per household)              7.869261e-13
#> 7         Domain calibration (by region)              3.552714e-15

Trimmed calibration matches ReGenesees

Range-restricted trimming of calibrated weights (step_trim_calibrated()) also agrees with an established reference: ReGenesees trimcal(). Both take absolute bounds on the final weight (weightflow lower/upper, ReGenesees w.range), trim to that range while preserving the calibration totals, and return the same solution. On a calibration to ~ sex + age trimmed to a fixed interval, the two agree to machine precision (maximum absolute weight difference about 1e-8, correlation 1), with both preserving the population totals. The comparison is not run here because ReGenesees is not a dependency; the script trim_calibrated_vs_regenesees.R reproduces it.

# weightflow
w_wf <- muestra |>
  weighting_spec(base_weights = pw) |>
  step_calibrate(method = "linear", formula = ~ sex + age, totals = pop_tot) |>
  step_trim_calibrated(~ sex + age, lower = lo, upper = up, calfun = "linear") |>
  prep() |> collect_weights(drop_zero = FALSE)

# ReGenesees
des   <- ReGenesees::e.svydesign(data = muestra, ids = ~ id, weights = ~ pw)
templ <- ReGenesees::fill.template(universe = universo,
           template = ReGenesees::pop.template(des, calmodel = ~ sex + age))
cal   <- ReGenesees::e.calibrate(des, df.population = templ, calfun = "linear")
w_rg  <- weights(ReGenesees::trimcal(cal, w.range = c(lo, up)))

What weightflow adds

The point of agreement is trust: where the methods overlap, weightflow returns exactly what survey does. On top of that shared core, weightflow contributes the staged cascade (unknown eligibility, ineligible dropping, within-household selection, and person- or household-level nonresponse, each as a pipeable step with diagnostics), the tidy totals and domain interfaces shown above, and a bootstrap that re-applies the whole recipe on each replicate, so the variance reflects every adjustment (see the Variance estimation article). For design-based inference you can always export the final weights back to survey/srvyr. ```

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.