plots.Full {BDEsize} | R Documentation |
Diagnosis Graphs for Sample Size of Full Factorial Design
Description
This function produces graphs between the sample size, power and the detectable standardized effect size of full factorial design.
Usage
plots.Full(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 |
delta_type |
specifies the type of standardized effect size: 1 for standard deviation type and 2 for range type. |
delta |
vector of effect sizes: |
deltao |
the minimal detectable standardized effect size for power vs the sample size plot when |
alpha |
Type I error. |
beta |
Type II error. |
type |
graph type: 1 for Power vs Delta plot, 2 for Delta vs Sample size plot, and 3 for Power vs Sample size plot. |
maxsize |
tolerance for sample size. |
Details
This function produces graph between the sample size, power 1-beta
and the detectable standardized effect size delta
of full factorial design.
According to type
, it displays plot of Power vs Delta, Delta vs Sample size, or Power vs Sample size.
Value
plot of Power vs Delta, Delta vs Sample size, or Power vs Sample size according to type
.
See Also
plots.2levFr
, plots.Split
, plots.Block
.
Examples
# plot of Power vs Delta for full factorial design
# with 2 factors without the interaction effects
plots.Full(factor.lev=c(2, 3), interaction=FALSE,
delta_type=1, delta=c(1, 0, 1), alpha=0.05, beta=0.2, type=1)
# plot of Power vs Sample size for full factorial design
# with 2 factors with the interaction effects
plots.Full(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)