pareto_plot {ggDoE}R Documentation

Pareto Plot of Effects

Description

Pareto Plot of Effects

Usage

pareto_plot(
  model,
  alpha = 0.05,
  method = "Lenth",
  margin_errors = TRUE,
  showplot = TRUE
)

Arguments

model

Model of class "lm"

alpha

specify the significance level to compute margin of errors. Numeric significance level, between 0 and 1. Default is alpha=0.05

method

Character value. Method to calculate PSE. Default is Lenth. Options include: Zahn, WZahn, Lenth, RMS, Dong, JuanPena, Daniel. See Details.

margin_errors

Default is TRUE, if false the cutoffs for margin of errors (ME) and simultaneous margin of error (SME) are not shown

showplot

Default is TRUE, if false plot will not be shown and a tibble is returned with data used to create the pareto plot

Details

The method argument is a simple wrapper for the function PSE() from the unrepx R package. For more details you can use ?unrepx::PSE(). The method arguement implements methods of estimating the standard error of effects estimates from unreplicatd designs. The methods include

Value

A bar plot with ordered effects, margin of error (ME) and simultaneous margin of error (SME) cutoffs.

References

Daniel, C (1959) Use of Half-Normal Plots in Interpreting Factorial Two-Level Experiments. Technometrics, 1(4), 311-341

Dong, F (1993) On the Identification of Active Contrasts in Unreplicated Fractional Factorials. Statistica Sinica 3, 209-217

Hamada and Balakrishnan (1998) Analyzing Unreplicated Factorial Experiments: A Review With Some New Proposals. Statistica Sinica 8, 1-41

Juan, J and Pena, D (1992) A Simple Method to Identify Significant Effects in Unreplicated Two-Level Factorial Designs. Communications in Statistics: Theory and Methods 21, 1383-1403

Lenth, R (1989) Quick and Easy Analysis of Unrelicated Factorials Technometrics 31(4), 469-473

Zahn, D (1975) Modifications of and Revised Critical Values for the Half-Normal Plot. Technometrics 17(2), 189-200

Examples

m1 <- lm(lns2 ~ (A+B+C+D)^4,data=original_epitaxial)
pareto_plot(m1)
pareto_plot(m1,method='Zahn',alpha=0.1)
pareto_plot(m1,margin_errors=FALSE)

[Package ggDoE version 0.7.8 Index]