el_pairwise {elgbd}R Documentation

Pairwise comparisons for general block designs with empirical likelihood

Description

Tests all pairwise comparisons or comparisons with control for general block designs with empirical likelihood. Two single step asymptotic kk-FWER (generalized family-wise error rate) controlling procedures are available: asymptotic Monte Carlo (AMC) and nonparametric bootstrap (NB).

Usage

el_pairwise(
  formula,
  data,
  control = NULL,
  k = 1L,
  alpha = 0.05,
  method = c("AMC", "NB"),
  B,
  nthreads = 1L,
  maxit = 10000L,
  abstol = 1e-08,
  verbose = FALSE
)

Arguments

formula

An object of class formula (or one that can be coerced to that class) for a symbolic description of the model to be fitted. It must specify the variables for response, treatment, and block as response ~ treatment | block. Note that the use of vertical bar (|) separating treatment and block.

data

A data frame, list or environment (or object coercible by as.data.frame() to a data frame) containing the variables in formula.

control

An optional single character that specifies the treatment for comparisons with control.

k

A single integer for kk in kk-FWER. Defaults to 1.

alpha

A single numeric for the overall significance level. Defaults to 0.05.

method

A single character for the procedure to be used; either AMC or NB is supported. Defaults to AMC.

B

A single integer for the number of Monte Carlo samples for the AMC (number of bootstrap replicates for the NB).

nthreads

A single integer for the number of threads for parallel computation via 'OpenMP' (if available). Defaults to 1.

maxit

A single integer for the maximum number of iterations for constrained minimization of empirical likelihood. Defaults to 10000.

abstol

A single numeric for the the absolute convergence tolerance for optimization. Defaults to 1e-08.

verbose

A single logical. If TRUE, a message on the convergence status is printed. Defaults to FALSE.

Value

A list containing the model fit and optimization results.

References

Kim E, MacEachern SN, Peruggia M (2023). "Empirical likelihood for the analysis of experimental designs." Journal of Nonparametric Statistics, 35(4), 709–732. doi:10.1080/10485252.2023.2206919.

Examples

# All pairwise comparisons
data("clothianidin")
el_pairwise(clo ~ trt | blk, data = clothianidin, B = 1000)

# Comparisons with control
el_pairwise(clo ~ trt | blk,
  control = "Naked", data = clothianidin, B = 1000
)

[Package elgbd version 0.9.0 Index]