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Compute sex-stratified standardized scores (SDS) for adiposity measures

Source: R/adiposity_sds_strat.R
adiposity_sds_strat.Rd

Computes sex-specific SDS (z-scores) for selected anthropometric variables using reference means and SDs provided separately for males and females.

Usage

adiposity_sds_strat(
  data,
  col_map,
  ref,
  na_action = c("keep", "omit", "error"),
  check_extreme = FALSE,
  extreme_action = c("cap", "NA", "error"),
  extreme_rules = NULL,
  allow_partial = FALSE,
  prefix = "",
  verbose = FALSE
)

Arguments

data

Data frame or tibble with variables and a sex column

col_map

Named list mapping:

  • sex: column name with sex values ("M","F","m","f", 1, 2)

  • vars: optional named list mapping reference variable names -> data column names. If omitted, expects the reference variable names to exist in data.

ref

Named list with elements "M" and "F". Each is a named list of numeric vectors c(mean=, sd=) keyed by variable name, e.g.: list( M = list(BMI = c(mean=23, sd=3.5), waist = c(mean=85, sd=10)), F = list(BMI = c(mean=21, sd=3.0), waist = c(mean=75, sd=9)) )

na_action

One of:

  • "keep" - keep rows with NA (propagates to outputs)

  • "omit" - drop rows with NA in any required variable

  • "error" - abort if any required variable has NA

check_extreme

Logical; if TRUE, screen raw variables for extremes before SDS

extreme_action

One of:

  • "cap" - winsorize to bounds

  • "NA" - set out-of-range to NA

  • "error" - abort on out-of-range

extreme_rules

Optional named list of c(min, max) per variable (raw scale). If NULL, broad defaults are applied for common measures (BMI, waist, weight, height, hip, WC, HC).

allow_partial

If TRUE, skip variables absent in data (with a warning); if FALSE error

prefix

Optional prefix for output columns (default "")

verbose

Logical; when TRUE emit progress via package logger; by default logging is controlled by options(healthmarkers.verbose)

Value

A tibble with one SDS column per retained variable: varname_SDS, where varname is the original variable name (optionally prefixed by prefix).

References

Cole TJ, Green PJ (1992). “Smoothing Reference Centile Curves: The LMS Method and Penalized Likelihood.” Statistics in Medicine, 11(10), 1305–1319. doi:10.1002/sim.4780111005 . ; World Health Organization (1995). Physical Status: The Use and Interpretation of Anthropometry, volume 854 of Technical Report Series. World Health Organization. ISBN 9241208546, No DOI for this WHO report; see ISBN/URL, https://www.who.int/publications/i/item/9241208546.

Examples

ref <- list(
  M = list(BMI = c(mean=24.5, sd=3.8), waist = c(mean=88, sd=12)),
  F = list(BMI = c(mean=22.1, sd=4.2), waist = c(mean=76, sd=11))
)
df <- data.frame(BMI=c(25.2,21.8,27.1), waist=c(85,72,95), sex=c("M","F","M"))
adiposity_sds_strat(
  df,
  col_map = list(sex = "sex", vars = list(BMI = "BMI", waist = "waist")),
  ref = ref
)
#> adiposity_sds_strat(): preparing inputs (2 vars, 3 rows)
#> adiposity_sds_strat(): column map: BMI -> 'BMI', waist -> 'waist'
#> adiposity_sds_strat(): results: BMI_SDS 3/3, waist_SDS 3/3
#> # A tibble: 3 × 2
#>   BMI_SDS waist_SDS
#>     <dbl>     <dbl>
#> 1  0.184     -0.25 
#> 2 -0.0714    -0.364
#> 3  0.684      0.583