df_netKmax2: An example of a row-dependent dataset with known network of...

In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description

Simulated dataset containing measured i.i.d. baseline covariate (W1), dependent binary exposure (A) and binary binary outcome (Y), along with a known network of friends encoded by strings on space separated friend IDs in Net_str. The 1,000 baseline covariates W1 were sampled as i.i.d., while the exposure value of A for each observation i was sampled conditionally on the value of i's baseline covariate W1[i], as well as the baseline covariate values of i's friends in Net_str. Similarly, the binary outcome Y for each observation was generated conditionally on i's exposure and baseline covariates values in (W1[i],A[i]), as well as the values of exposures and baseline covariates of i's friends in Net_str. Individual variables are described below.

Usage

data(df_netKmax2)

Format

A data frame with 1,000 dependent observations (rows) and 6 variables:

IDs

unique observation identifier

Y

binary outcome that depends on unit's baseline covariate value and exposure in W1, A, as well as the baseline covariate values and exposures W1, A of observations in the friend network Net_str

nFriends

number of friends for each observation (row), range 0-2

W1

binary baseline covariate (independent)

A

binary exposure status that depends on unit's baseline covariate value in W1, as well as the baseline covariate values W1 of observations in the friend network Net_str

Net_str

each observation is a string of space separated friend IDs (this can be either observation IDs or just space separated friend row numbers)

tmlenet documentation built on May 29, 2017, 2:22 p.m.