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

## ----setup--------------------------------------------------------------------
library(SPINA);

TSH <- c(1, 3.24, 0.7);
FT4 <- c(16.5, 7.7, 9);
FT3 <- c(4.5, 28, 6.2);
lu <- 20;

estimated.GT(TSH, FT4);

estimated.GD(FT4, FT3);

estimated.TTSI(TSH, FT4, lu)

estimated.TSHI(TSH, FT4)

estimated.sTSHI(TSH, FT4, mean = 2.7, sd = 0.676)

estimated.sGD(FT4, FT3, mean = 30, sd = 5)

SPINA.GT(TSH, FT4)

SPINA.GD(FT4, FT3)

SPINA.sGD(FT4, FT3, mean = 30, sd = 5)

TSH <- 0.86;
T4 <- 163;
T3 <- 3.0;

estimated.GTT(TSH, T4)

estimated.GDTT(T4, T3)

SPINA.GTT(TSH, T4)

SPINA.GDTT(T4, T3)

Insulin <- 63.01;
Glucose <- 4.34;

SPINA.GBeta(Insulin, Glucose)

SPINA.GR(Insulin, Glucose)

SPINA.DI(Insulin, Glucose)

HOMA.IR(Insulin, Glucose)

HOMA.Beta(Insulin, Glucose)

HOMA.IS(Insulin, Glucose)

QUICKI(Insulin, Glucose)


## -----------------------------------------------------------------------------
library(SPINA);

TSH <- c(1, 3.24, 0.7);
FT4 <- c(16.5, 7.7, 9);
FT3 <- c(4.5, 28, 6.2);

print(paste("GT^:", SPINA.GT(TSH, FT4)));
print(paste("GD^:", SPINA.GD(FT4, FT3)));
print(paste("sGD^:", SPINA.sGD(FT4, FT3)));

# Source:
# Pilo A, Iervasi G, Vitek F, Ferdeghini M, Cazzuola F,
# Bianchi R. Thyroidal and peripheral production of 
# 3,5,3'-triiodothyronine in humans by multicompartmental 
# analysis. Am J Physiol. 1990 Apr;258(4 Pt 1):E715-26. 
# PMID 2333963.

# BSA: body surface area in m^2
# IDV: initial distribution volume
# TT4: total T4 in mcg/dl
# TT3: total T3 in ng/ml
# FT4: free T4 in pg/ml
# FT3: free T3 in pg/ml
# TT4.SI: total T4 in nmol/l
# TT3.SI: total T3 in nmol/l
# FT4.SI: free T4 in pmol/l
# FT3.SI: free T3 in pmol/l
# SR: secretion rate
# CR.F, CR.S and CR.T: conversion rate (fast pool, slow pool and total)
# PAR: plasma apperance rate
# PR: production rate
# CR.6, CR.2, CR.0: conversion ratio from 6-compartment, 2-compartment and noncompartmental model

t3.mc <- data.frame(Sex = c("m", "f", "m", "m", "m", "m", "m", "m", "f", "m", "f", "m", "f", "f"), 
Age = c(54, 43, 31, 65, 44, 26, 27, 19, 53, 36, 48, 20, 44, 59),
Body.Mass = c(83, 68.5, 83, 69, 75, 73, 82, 63, 66.5, 72, 53, 65.5, 63, 60),
BSA = c(2.02, 1.7, 2.02, 1.73, 1.8, 1.9, 1.98, 1.8, 1.69, 1.81, 1.49, 1.79, 1.75, 1.57),
IDV.T4 = c(3801, 2272, 2686, 2726, 2632, 2804, 2770, 3119, 2749, 2860, 2467, 3624, 2965, 2327),
TT4 = c(8, 10.4, 7.9, 8, 8.8, 6.5, 7.7, 7.2, 6.6, 8.3, 9.5, 6.5, 7.9, 9.6),
TT4.SI = c(8, 10.4, 7.9, 8, 8.8, 6.5, 7.7, 7.2, 6.6, 8.3, 9.5, 6.5, 7.9, 9.6) * 12.87,
TT3 = c(1.23, 1.1, 1.32, 1.03, 1.33, 1.07, 1.4, 1.2, 1.36, 1.08, 1.21, 1.26, 1.21, 1.02),
TT3.SI = c(1.23, 1.1, 1.32, 1.03, 1.33, 1.07, 1.4, 1.2, 1.36, 1.08, 1.21, 1.26, 1.21, 1.02) * 1.54,
FT4 = c(10.1, 10.5, 8.8, 13.1, 11.1, 8.8, 10.2, 8.9, 6.9, 9, 10.5, 10.4, 11.8, 8.4),
FT4.SI = c(10.1, 10.5, 8.8, 13.1, 11.1, 8.8, 10.2, 8.9, 6.9, 9, 10.5, 10.4, 11.8, 8.4) * 1.287,
FT3 = c(4.3, 5.7, 4.3, 4.4, 3.5, 3.4, 4.1, 4.1, 3.6, 2.6, 3.8, 4.1, 5, 4.3),
FT3.SI = c(4.3, 5.7, 4.3, 4.4, 3.5, 3.4, 4.1, 4.1, 3.6, 2.6, 3.8, 4.1, 5, 4.3) * 1.54,
TSH = c(1.2, 1.4, 1, 1.8, 1.5, 1.5, 2, 1.9, 1.4, 1.8, 1.1, 1.3, 2, 1.5),
SR.T4 = c(44.5, 55.5, 45.3, 59.3, 59.2, 41.5, 50.9, 57.7, 54.3, 51.8, 76.5, 61.1, 65.7, 62.9), 
SR.T3 = c(1.98, 4.45, 7.05, 3.14, 5.95, 5.31, 3.51, 4.15, 0.91, 1.4, 2.89, 2.47, 2.64, 0.88),
CR.F.mean = c(12.3, 6.71, 8.73, 9.32, 8.75, 9.14, 10.6, 9.35, 7.9, 12.6, 13.1, 17.3, 14.5, 9.07),
CR.S.mean = c(3.7, 1.11, 1.06, 0.18, 1.2, 0.52, 0.4, 6.74, 3.39, 0.38, 1.55, 2.02, 2.37, 3.64),
CR.T.mean = c(12.3, 6.71, 8.73, 9.32, 8.75, 9.14, 10.6, 9.35, 7.9, 12.6, 13.1, 17.3, 14.5, 9.07) + c(3.7, 1.11, 1.06, 0.18, 1.2, 0.52, 0.4, 6.74, 3.39, 0.38, 1.55, 2.02, 2.37, 3.64),
PAR.T3 = c(16.7, 11.8, 16.4, 12.4, 15.5, 14.7, 14.2, 18.8, 11.4, 14.1, 16.9, 21.1, 18.5, 12.6),
PR.T3.mean = c(18, 12.3, 16.8, 12.6, 15.9, 15, 14.5, 20.2, 12.2, 14.4, 17.5, 21.8, 19.5, 13.6),
PR.T3 = c(20, 14.4, 18.9, 15.2, 17.6, 17.3, 16.1, 21.2, 12.9, 16.3, 19.8, 24.1, 22.4, 14.6),
CR.S = c(12.3, 9.02, 11.7, 9.51, 10.6, 10.5, 9.5, 12.8, 7.74, 9.78, 12.2, 14.4, 13.9, 8.87),
CR.6 = c(42.9, 16.9, 25.8, 19.2, 20.1, 27.8, 25.8, 33.4, 24.8, 30, 22.8, 37.9, 30.7, 24.1),
CR.2 = c(41, 16.5, 25.7, 19.4, 19.9, 27.8, 25.8, 31.3, 23.9, 30.1, 22.7, 37.6, 30.2, 23.1),
CR.0 = c(39.3, 15.6, 24.2, 18.5, 19.1, 26.9, 25.2, 30.8, 22.5, 29.1, 21.5, 35, 28.5, 22.5),
QP = c(2.31, 1.47, 1.76, 1.62, 1.95, 1.58, 1.96, 2.08, 2.21, 1.71, 2, 2.55, 2.06, 1.51),
QF = c(3.22, 2.85, 2.86, 3.55, 2.55, 3.04, 2.87, 3.66, 3.31, 2.9, 3.18, 3.94, 4.35, 2.82),
QS = c(25.8, 18.3, 22.6, 19, 15.4, 14.2, 14.8, 19.4, 18, 18.7, 15.4, 30.1, 25.7, 17.8),
QT = c(31.3, 22.6, 27.3, 24, 19.9, 18.8, 19.6, 25.2, 23.5, 23.3, 20.6, 36.6, 32.2, 22.2)
)

t3.mc$GT <- SPINA.GT(t3.mc$TSH, t3.mc$FT4.SI);
t3.mc$GD <- SPINA.GD(t3.mc$FT4.SI, t3.mc$FT3.SI);

print(t3.mc$GT);
print(t3.mc$GD);

Insulin <- 63.01;
Glucose <- 4.34;

print(paste("GBeta^:", SPINA.GBeta(Insulin, Glucose)));
print(paste("GR^:", SPINA.GR(Insulin, Glucose)));
print(paste("DI:", SPINA.DI(Insulin, Glucose)));