Cholesterol: Framingham cholesterol study

In qrLMM: Quantile Regression for Linear Mixed-Effects Models

Framingham cholesterol study

Description

The Framingham cholesterol study generated a benchmark dataset (Zhang and Davidian, 2001) for longitudinal analysis to examine the role of serum cholesterol as a risk factor for the evolution of cardiovascular disease for 200 randomly selected subjects.

Usage

data(Cholesterol)

Format

This data frame contains the following columns:

newid

a numeric vector indicating the subject on which the measurement was made. It represents the subject number in the sample.

ID

a numeric vector indicating the subject on which the measurement was made. It represents the subject number in the population.

cholst

cholesterol level for patient newid.

sex

a dichotomous gender (0=female, 1=male).

age

age of the patient in years.

year

years elapsed since the start of the study to the current measurement.

Source

Zhang, D., & Davidian, M. (2001). Linear mixed models with flexible distributions of random effects for longitudinal data. Biometrics, 57(3), 795-802.

References

https://www.framinghamheartstudy.org/about-fhs/background.php

Examples

## Not run: 
data(Cholesterol)
attach(Cholesterol)

y  = cholst            #response
x  = cbind(1,sex,age)  #design matrix for fixed effects
z  = cbind(1,year)     #design matrix for random effects

#A median regression
median_reg = QRLMM(y,x,newid,nj,MaxIter = 500)

## End(Not run)

qrLMM documentation built on Sept. 11, 2024, 6:49 p.m.