diff_eff.crd: Completely Randomized Design

In acAnik10/DoEtoolsR: Handy Tools for Design of Experiments in R

Completely Randomized Design

Description

Test for Significant Treatment effects and Differential Effects of Treatment Contrasts for a CRD

Usage

diff_eff.crd(resp, trt, los = 0.05)

Arguments

resp	The response variable vector
trt	The treatment vector
los	Level of significance (Default is 0.05)

Details

In experimental designs if test of differential effect gets rejected we might be interested in analyzing which pair of treatments is behind the rejection. For CRD we need to calculate the estimate of the treatment contrast (tau_i - tau_i') and test H0: tau_i = tau_i' for plausible rejection. The estimate of (tau_i - tau_i') is (y_i0 - y_i'0) which follows N(tau_i - tau_i', sigma^2 * (1/n_i + 1/n_i')). Under H_0, the test statistic (y_i0 - y_i'0)/sqrt(MSE*(1/n_i + 1/n_i')) follows t_n-v.

Value

A list containing - ANOVA Table, Decision Table, Rejected Pairs, Mean Square Error, Critical Point

Special Thanks

Professor Surupa Chakraborty and Professor Debjit Sengupta for helping me in building the concepts of Design of Experiments. Professor Madhura Dasgupta for guiding me in R programming.

Author(s)

Anik Chakraborty

See Also

For RBD diff_eff.rbd, for LSD diff_eff.lsd

acAnik10/DoEtoolsR documentation built on June 2, 2022, 12:36 p.m.