Compute all available HealthMarkers categories

Usage

all_health_markers(
  data,
  col_map,
  which = "all",
  include_insulin = TRUE,
  normalize = c("none", "z", "inverse", "range", "robust"),
  mode = c("both", "IS", "IR"),
  verbose = TRUE,
  na_action = c("keep", "omit", "error"),
  id_col = NULL,
  return_input = TRUE
)

Arguments

data: A data.frame or tibble.
col_map: Named list for column mapping forwarded to underlying functions. If col_map is NULL or an empty list, all_health_markers() calls hm_col_report() once at the top level to guess a column map from common synonyms (for example TG vs triglycerides, BMI vs bmi, HDL_c vs HDL). The inferred col_map is then reused for all groups that require it, and an error is thrown if required keys (e.g. TG, HDL_c, LDL_c, TC, BMI, age, sex) cannot be inferred.
which: "all" or a vector of registry keys (see Details).
include_insulin: Logical; include all_insulin_indices() first.
normalize: One of c("none","z","inverse","range","robust").
mode: One of c("both","IS","IR") passed to insulin indices.
verbose: Logical.
na_action: One of c("keep","omit","error"); forwarded to underlying calculators (HM-CS v2).
id_col: Optional character string naming a column in data to include in the returned output when return_input = FALSE. Ignored when return_input = TRUE. Typical use: participant ID or sample barcode.
return_input: Logical (default TRUE). When TRUE the original input columns are retained in the output (current behaviour). When FALSE only the newly computed marker columns are returned, plus id_col if supplied. Set to FALSE to avoid carrying a large input data frame through the pipeline — join back later with cbind() or dplyr::left_join() on id_col.

Value

Data frame. When return_input = TRUE (default): original columns plus all derived markers. When return_input = FALSE: only the newly computed columns (and id_col if specified).

Details

Common group names for which include:

"lipid", "liver", "glycemic", "mets", "oxidative"
"bone", "allostatic_load", "nutrient", "vitamin", "vitamin_d_status"
"renal", "ckd_stage", "kidney_kfre"
"frailty_index", "charlson", "sarc_f"
"nfl", "iAge", "calcium_corrected", "kyn_trp"

Note

For academic / clinical references tied to each derived marker or index, consult the help pages of the source functions (e.g. ?allostatic_load, ?bone_markers, ?vitamin_markers, ?inflammatory_markers, etc.). This aggregator provides integration only and does not restate citations.

Aggregator wrapper. See underlying function help pages for full references across categories included by which.

Examples

# Quick smoke-test (lipid group only, no insulin)
df <- data.frame(TC = 200, HDL_c = 50, TG = 150, LDL_c = 120)
all_health_markers(df, col_map = list(), which = "lipid",
                   include_insulin = FALSE, normalize = "none",
                   verbose = FALSE, na_action = "keep")
#>    TC HDL_c  TG LDL_c     VLDL non_HDL remnant_c       AIP CRI_I CRI_II
#> 1 200    50 150   120 68.18182     150        30 0.4771213     4    2.4
#>   HDL_TG_ratio LDL_HDL_ratio non_HDL_c ratio_TC_HDL ratio_TG_HDL ratio_LDL_HDL
#> 1    0.3333333           2.4       150            4            3           2.4
#>   ApoB_ApoA1
#> 1         NA

# \donttest{
# Lipid + liver groups
df <- data.frame(
  TC = 200, HDL_c = 50, TG = 150, LDL_c = 120,
  ALT = 30, AST = 20, BMI = 25
)
all_health_markers(df, col_map = list(), which = c("lipid","liver"),
                   include_insulin = FALSE, normalize = "none", mode = "both",
                   verbose = FALSE, na_action = "keep")
#>    TC HDL_c  TG LDL_c ALT AST BMI     VLDL non_HDL remnant_c       AIP CRI_I
#> 1 200    50 150   120  30  20  25 68.18182     150        30 0.4771213     4
#>   CRI_II HDL_TG_ratio LDL_HDL_ratio non_HDL_c ratio_TC_HDL ratio_TG_HDL
#> 1    2.4    0.3333333           2.4       150            4            3
#>   ratio_LDL_HDL ApoB_ApoA1 FLI NFS APRI FIB4 BARD ALBI MELD_XI
#> 1           2.4         NA  NA  NA   NA   NA    0   NA      NA
# }