BDEsize (version 1.6)

plots.Block: Diagnosis Graphs for the number of Blocks of Randomized Complete Block Design

Description

This function produces graphs between the sample size, power and the detectable standardized effect size of randomized complete block design.

Usage

plots.Block(factor.lev, interaction = FALSE, delta_type = 1, delta = c(1, 0, 1), 
    deltao = NULL, alpha = 0.05, beta = 0.2, type = 1, maxsize = 1000)

Arguments

factor.lev

vector of the numbers of levels for each factor.

interaction

specifies whether two-way interaction effects are included in a model with the main effects. When interaction = TRUE, two-way interaction effects are include in a model.

delta_type

specifies the type of standardized effect size: 1 for standard deviation type and 2 for range type.

delta

vector of effect sizes: delta[1] for main effects, delta[2] for two-way interaction effects, and delta[3] for standard deviation of noise. When interaction=FALSE, delta[2] is 0.

deltao

the minimal detectable standardized effect size for power vs the number of blocks plot when type = 3.

alpha

Type I error.

beta

Type II error.

type

graph type: 1 for Power vs Delta plot, 2 for Delta vs the Number of Blocks plot, and 3 for Power vs the Number of Blocks plot.

maxsize

tolerance for the number of blocks.

Value

plot of Power vs Delta, Delta vs Number of Blocks, or Power vs Number of Blocks according to type.

Details

In a randomized complete block design (without replications), the optimal number of blocks need to be determined. This function produces graph between Number of Block, power 1-beta and the detectable standardized effect size delta of randomized complete block design. According to type, it displays plot of Power vs Delta, Delta vs Number of Blocks, or Power vs Number of Blocks.

See Also

plots.Full, plots.2levFr, plots.Split.

Examples

# NOT RUN {
# plot of Power vs Delta for randomized complete block design 
# with 2 factors without the interaction effects
plots.Block(factor.lev=c(2, 2), interaction=FALSE,
    delta_type=1, delta=c(1, 0, 1), alpha=0.05, beta=0.2, type=1)
    
# plot of Power vs Number of Blocks for randomized complete block design 
# with 2 factors with the interaction effects
plots.Block(factor.lev=c(2, 3), interaction=TRUE,
    delta_type=1, delta=c(1, 1, 1), deltao=1.5, alpha=0.05, beta=0.2, type=3)
# }