designD: Design: Gaussian responses (unknown heteroscedasticity)
In metagen: Inference in Meta Analysis and Meta Regression

Description Usage Arguments Details Value Examples

View source: R/methods-for-data-generation.R

Method for generating a sampling design for data generation following a random effects meta regression model with unknown heteroscedasticity.

1	designD(n, h_bounds, d_bounds, s_bounds, x)

`n`	resolution of the heterogeneity and heteroscedasticity parameters, i.e., the number of of different (heterogeneity, heteroscedasticity, sizes) tuple in the design.
`h_bounds`	bounds of the heterogeneity.
`d_bounds`	bounds of the heteroscedasticity.
`s_bounds`	bounds of the study sizes.
`x`	design matrix.

Generates a sampling design for the heterogeneity 'h', heteroscedasticity 'd1', ..., 'dk', and study sizes 's1', ..., 'sk'. This design can be used for testing methods that adjust for uncertainty in the heteroscedasticity estimates by additionally considering the size of the respected studies.

Points in the design are selected via a maxi-min hypercube sampling using the 'lhs' package in a predefined parameter cube.

Function returns a data frame. Each line of this data frame can be an input to the function 'rD' which is used to sample data from such a design.

dD <- designD(n=15L, h_bounds=c(0,1), d_bounds=c(0.01,2),
  s_bounds=c(200L,2000L), x=cbind(1,1:7))

if(!all(dim(dD) == c(15,2*dim(cbind(1,1:7))[1]+1))) {
  stop("Wrong dimension")
}