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Last data update: 2014.03.03

R: simulate data

datagenerator

R Documentation

simulate data

Description

Simulate data for demonstration of flarcc.

Usage

datagenerator(n = n, beta0 = beta0, family = "gaussian", seed = seed)

Arguments

`n`	sample size for each study, a vector of length `K`, the number of studies; can also be an scalar, to specify equal sample size
`beta0`	coefficient matrix, with dimension `K * p`, where K is the number of studies and p is the number of covariates
`family`	"gaussian" for continuous response, "binomial" for binary response, "poisson" for count response
`seed`	set random seed for data generation

Details

These data sets are artifical, and used to test out some features of flarcc.

Value

a simulated data frame will be returned, containing y, X, and study ID sid

Examples

n <- 200    # sample size in each study
K <- 10     # number of studies
p <- 3      # number of covariates in X (including intercept)
N <- n*K    # total sample size

# the coefficient matrix, used this to set desired heterogeneous pattern (depends on p and K)
beta0 <- matrix(c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, # intercept
                  0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0, # beta_1, etc.
                  0.0,0.0,0.0,0.0,0.5,0.5,0.5,1.0,1.0,1.0), K, p)

# generate a data set, family=c("gaussian", "binomial", "poisson")
data <- datagenerator(n=n, beta0=beta0, family="gaussian", seed=123)