ggm (version 2.5)

derived: Data on blood pressure body mass and age

Description

Raw data on blood pressure, body mass and age on 44 female patients, and covariance matrix for derived variables.

Usage

data(derived)

Format

A list containing a dataframe raw with 44 lines and 5 columns and a symmetric 4x4 covariance matrix S.

The following is the description of the variables in the dataframe raw

Sys

Systolic blood pressure, in mm Hg

Dia

Diastolic blood pressure, in mm Hg

Age

Age of the patient, in years

Hei

Height, in cm

Wei

Weight, in kg

The following is the description of the variables for the covariance matrix S.

Y

Derived variable Y=log(Sys/Dia)

X

Derived variables X=log(Dia)

Z

Body mass index Z=Wei/(Hei/100)^2

W

Age

References

Wermuth N. and Cox D.R. (1995). Derived variables calculated from similar joint responses: some characteristics and examples. Computational Statistics and Data Analysis, 19, 223-234.

Examples

# A DAG model with a latent variable U
G = DAG(Y ~ Z + U, X ~ U + W, Z ~ W)

data(derived)

# The model fitted using the derived variables
out = fitDagLatent(G, derived$S, n = 44, latent = "U")

# An ancestral graph model marginalizing over U
H = AG(G, M = "U")

# The ancestral graph model fitted obtaining the 
# same result
out2 = fitAncestralGraph(H, derived$S, n = 44)