ClustMC (version 0.1.1)

bss_test: Bautista, Smith and Steiner test for multiple comparisons

Description

Bautista, Smith and Steiner (BSS) test for multiple comparisons. Implements a procedure for grouping treatments following the determination of differences among them. First, a cluster analysis of the treatment means is performed and the two closest means are grouped. A nested analysis of variance from the original ANOVA is then constructed with the treatment source now partitioned into "groups" and "treatments within groups". This process is repeated until there are no differences among the group means or there are differences among the treatments within groups.

Usage

bss_test(
  y,
  trt,
  alpha = 0.05,
  show_plot = TRUE,
  console = TRUE,
  abline_options,
  ...
)

Value

A list with three data.frame and one hclust:

stats

data.frame containing summary statistics by treatment.

groups

data.frame indicating the group to which each treatment is assigned.

parameters

data.frame with the values used for the test. treatments is the total number of treatments and alpha is the significance level used.

dendrogram_data

object of class hclust with data used to build the dendrogram.

Arguments

y

Either a model (created with lm() or aov()) or a numerical vector with the values of the response variable for each unit.

trt

If y is a model, a string with the name of the column containing the treatments. If y is a vector, a vector of the same length as y with the treatments for each unit.

alpha

Numeric value corresponding to the significance level of the test. The default value is 0.05.

show_plot

Logical value indicating whether the constructed dendrogram should be plotted or not.

console

Logical value indicating whether the results should be printed on the console or not.

abline_options

list with optional arguments for the line in the dendrogram.

...

Optional arguments for the plot() function.

Author

Santiago Garcia Sanchez

References

Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997). A Cluster-Based Approach to Means Separation. Journal of Agricultural, Biological, and Environmental Statistics, 2(2), 179-197. tools:::Rd_expr_doi("doi:10.2307/1400402")

Examples

data("PlantGrowth")
# Using vectors -------------------------------------------------------
weights <- PlantGrowth$weight
treatments <- PlantGrowth$group
bss_test(y = weights, trt = treatments, show_plot = FALSE)
# Using a model -------------------------------------------------------
model <- lm(weights ~ treatments)
bss_test(y = model, trt = "treatments", show_plot = FALSE)