designD: Design: Gaussian responses (unknown heteroscedasticity)

Description Usage Arguments Details Value Examples

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

Description

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

Usage

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

Arguments

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.

Details

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.

Value

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.

Examples

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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")
}

metagen documentation built on May 29, 2017, 7:13 p.m.