## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(pft)

## -----------------------------------------------------------------------------
patient <- data.frame(
  sex = "M", age = 45, height = 178,
  dlco_measured   = 22.0,
  va_measured     = 5.8,
  kco_tr_measured = 3.79
)
out <- pft_diffusion(patient)
out[, grep("dlco|va|kco", colnames(out), value = TRUE)]

## -----------------------------------------------------------------------------
# Anemic adult male: corrected DLCO is higher than measured.
pft_dlco_hb_correct(dlco = 20.0, hemoglobin = 110, sex = "M", age = 45)

# Polycythemic adult male: corrected is lower than measured.
pft_dlco_hb_correct(dlco = 25.0, hemoglobin = 180, sex = "M", age = 45)

## -----------------------------------------------------------------------------
mixed_cohort <- data.frame(
  dlco_zscore   = c(-0.5, -2.0, -2.5, -2.5, -2.0,  0.0),
  va_zscore     = c(-0.5, -0.5, -2.0, -2.5, -0.5,  0.0),
  kco_tr_zscore = c(-0.5, -2.0,  0.0, -2.5,  0.5,  2.0)
)
pft_diffusion_interpret(mixed_cohort)

## -----------------------------------------------------------------------------
patient2 <- data.frame(
  sex = "F", age = 60, height = 165, race = "Caucasian",
  fev1_measured    = 1.6, fvc_measured    = 1.9,
  fev1fvc_measured = 0.84, tlc_measured   = 4.0,
  dlco_measured   = 10.0, va_measured    = 3.5,
  kco_tr_measured = 2.86
)
r <- pft_interpret(patient2)
r[, c("ats_classification", "diffusion_category")]

## ----eval = requireNamespace("dplyr", quietly = TRUE)-------------------------
library(dplyr)
cohort <- data.frame(
  sex    = c("M","F","M","F","M","F"),
  age    = c(45,60,30,55,70,28),
  height = c(178,165,175,160,170,180),
  race   = "Caucasian",
  fev1_measured    = c(2.5, 1.8, 4.0, 1.5, 2.2, 3.8),
  fvc_measured     = c(3.8, 2.4, 5.2, 2.5, 3.5, 5.0),
  tlc_measured     = c(6.0, 4.5, 6.8, 4.0, 6.5, 7.0),
  dlco_measured    = c(20.0, 12.5, 28.0, 10.0, 18.0, 25.0),
  va_measured      = c(5.8, 4.0, 6.5, 3.5, 5.5, 6.0),
  kco_tr_measured  = c(3.5, 3.2, 4.3, 2.9, 3.3, 4.0)
)
pft_interpret(cohort) |>
  count(sex, diffusion_category)