example_data {InsuSensCalc}

Example Dataset

Description

Names, description and units (where needed) of the variables. Name of the variables in the input data should be the same as the ones listed below for accurately calculating the indices. Otherwise it will result in Error. If a variable is missing for the category it will not calculate the any of the index for that category. This can be handeld by creating the variable column with NA vlaues If the values are missing for a variable it will set the value to NA and calculate the remaining indices and return the NA value for the missing variable.

Usage

example_data

Format

A data frame with rows (number of observations) and 17 columns (variables, can vary for every data):

age

numeric Age of the individual (years)

sex

factor Sex of the individual (1 for male, 2/0 for female)

I0

numeric Fasting insulin level (pmol/L)

G0

numeric Fasting glucose level (mmol/L)

I30

numeric Insulin level at 30 minutes (pmol/L)

G30

numeric Glucose level at 30 minutes (mmol/L)

I120

numeric Insulin level at 120 minutes (pmol/L)

G120

numeric Glucose level at 120 minutes (mmol/L)

HDL_c

numeric HDL cholesterol level (mmol/L)

FFA

numeric Free fatty acid level (mmol/L)

waist

numeric Waist circumference of the individual (cm)

weight

numeric Weight of the individual (kg)

bmi

numeric Body mass index of the individual (kg/m^2)

TG

numeric Triacylglycerides level (mmol/L)

rate_palmitate

numeric Rate of palmitate (arbitrary units)

rate_glycerol

numeric Rate of glycerol (arbitrary units)

fat_mass

numeric Fat mass of the individual (kg)

Source

Data is a simulated dataset for illustrative purposes.