Package 'pwr2'

Title: Power and Sample Size Analysis for One-way and Two-way ANOVA Models
Description: User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.
Authors: Pengcheng Lu, Junhao Liu, Devin Koestler
Maintainer: Pengcheng Lu <[email protected]>
License: GPL-2
Version: 1.0
Built: 2024-12-16 06:50:50 UTC
Source: CRAN

Help Index


Power and Sample Size Analysis for One-way and Two-way ANOVA Models

Description

User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

Details

Package: SPA
Type: Package
Version: 1.0
Date: 2017-05-01
License: GPL-2

There are major five functions in the package. The pwr.1way and pwr.2way functions provide the power analysis for one-way and two-way ANOVA models. The ss.1way and ss.2way functions provide the sample size calculation for one-way and two-way ANOVA models. The pwr.plot function illustrates drawing power curves for different parameter settings.

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

Maintainer:Pengcheng Lu <[email protected]>

References

[1] Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
pwr.2way(a=3, b=3, alpha=0.05, size.A=4, size.B=5, f.A=0.8, f.B=0.4)
pwr.2way(a=3, b=3, alpha=0.05, size.A=4, size.B=5, delta.A=4, delta.B=2, sigma.A=2, sigma.B=2)

## Example 2
ss.2way(a=3, b=3, alpha=0.05, beta=0.1, delta.A=1, delta.B=2, sigma.A=2, sigma.B=2, B=100)

## Example 3
n <- seq(2, 30, by=4)
f <- seq(0.1, 1.0, length.out=10)
pwr.plot(n=n, k=5, f=f, alpha=0.05)

Power calculation for balanced one-way ANOVA models

Description

Calculate power for one-way ANOVA models.

Usage

pwr.1way(k=k, n=n, alpha=alpha, f=NULL, delta=delta, sigma=sigma)

Arguments

k

Number of groups

n

Sample size per group

f

Effect size

alpha

Significant level (Type I error probability)

delta

The smallest difference among k groups

sigma

Standard deviation, i.e. square root of variance

Details

If effect size f is known, plug it in to the function; If delta and sigma are known instead of effect size, put NULL to f.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with "method" and "note" elements.

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

References

Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
pwr.1way(k=5, n=15, alpha=0.05, delta=1.5, sigma=1)
pwr.1way(k=5, n=15, f=NULL, alpha=0.05, delta=1.5, sigma=1)

## Example 2
pwr.1way(k=5, n=15, f=0.4, alpha=0.05)

Power calculation for balanced two-way ANOVA models

Description

Calculate power for two-way ANOVA models.

Usage

pwr.2way(a=a, b=b, alpha=alpha, size.A=size.A, size.B=size.B, f.A=NULL, f.B=NULL, 
delta.A=NULL, delta.B=NULL, sigma.A=NULL, sigma.B=NULL)

Arguments

a

Number of groups in Factor A

b

Number of groups in Factor B

alpha

Significant level (Type I error probability)

size.A

Sample size per group in Factor A

size.B

Sample size per group in Factor B

f.A

Effect size of Factor A

f.B

Effect size of Factor B

delta.A

The smallest difference among a groups in Factor A

delta.B

The smallest difference among b groups in Factor B

sigma.A

Standard deviation, i.e. square root of variance in Factor A

sigma.B

Standard deviation, i.e. square root of variance in Factor B

Details

If effect sizes f.A and f.B are known, plug them in to the function; If delta.A and sigma.A are known instead of f.A, put NULL to f.A. Similarly as delta.B and sigma.B.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with "method" and "note" elements.

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

References

Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
pwr.2way(a=3, b=3, alpha=0.05, size.A=4, size.B=5, f.A=0.8, f.B=0.4)

## Example 2
pwr.2way(a=3, b=3, alpha=0.05, size.A=4, size.B=5, delta.A=4, delta.B=2, sigma.A=2, sigma.B=2)
pwr.2way(a=3, b=3, alpha=0.05, size.A=4, size.B=5, f.A=NULL, f.B=NULL, 
delta.A=4, delta.B=2, sigma.A=2, sigma.B=2)

Power curves for different parameter settings (sample size and effect size) in balanced one-way ANOVA models

Description

Draw power curves for different parameter settings in balanced one-way ANOVA models.

Usage

pwr.plot(n=n, k=k, f=f, alpha=alpha)

Arguments

n

Sample size per group

k

Number of groups

f

Effect size

alpha

Significant level (Type I error probability)

Details

This function demonstrates drawing power curves for different sample size and effect size settings. N and f can be either a single value or a sequence of values, but they cannot be single values simultaneously. The combination of them could be (a sequence of n, a sequence of f), (a sequence of n, a single f), or (a single n, a sequence of f).

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

References

Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
n <- seq(2, 30, by=4)
f <- 0.5
pwr.plot(n=n, k=5, f=f, alpha=0.05)

## Example 2
n <- 20
f <- seq(0.1, 1.0, length.out=10)
pwr.plot(n=n, k=5, f=f, alpha=0.05)

## Example 3
n <- seq(2, 30, by=4)
f <- seq(0.1, 1.0, length.out=10)
pwr.plot(n=n, k=5, f=f, alpha=0.05)

Sample size calculation for balanced one-way ANOVA models

Description

Calculate sample size for one-way ANOVA models.

Usage

ss.1way(k=k, alpha=alpha, beta=beta, f=NULL, delta=delta, sigma=sigma, B=B)

Arguments

k

Number of groups

alpha

Significant level (Type I error probability)

beta

Type II error probability (Power=1-beta)

f

Effect size

delta

The smallest difference among k group

sigma

Standard deviation, i.e. square root of variance

B

Iteration times, default number is 100

Details

Beta is the type II error probability which equals 1-power. For example, if the target power is 85% (=0.85), the corresponding beta equals 0.15. If effect size f is known, plug it in to the function; If delta and sigma are known instead of effect size, put NULL to f, or just miss f argument.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with "method" and "note" elements.

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

References

Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
ss.1way(k=5, alpha=0.05, beta=0.1, f=1.5, B=100)

## Example 2
ss.1way(k=5, alpha=0.05, beta=0.1, delta=1.5, sigma=1, B=100)
ss.1way(k=5, alpha=0.05, beta=0.1, f=NULL, delta=1.5, sigma=1, B=100)

Sample size calculation for balanced two-way ANOVA models

Description

Calculate sample size for two-way ANOVA models.

Usage

ss.2way(a=a, b=b, alpha=alpha, beta=beta, f.A=NULL, f.B=NULL, 
delta.A=NULL, delta.B=NULL, sigma.A=NULL, sigma.B=NULL, B=B)

Arguments

a

Number of groups in Factor A

b

Number of groups in Factor B

alpha

Significant level (Type I error probability)

beta

Type II error probability (Power=1-beta)

f.A

Effect size of Factor A

f.B

Effect size of Factor B

delta.A

The smallest difference among a groups in Factor A

delta.B

The smallest difference among b groups in Factor B

sigma.A

Standard deviation, i.e. square root of variance in Factor A

sigma.B

Standard deviation, i.e. square root of variance in Factor B

B

Iteration times, default number is 100

Details

Beta is the type II error probability which equals 1-power. For example, if the target power is 85% (=0.85), the corresponding beta equals 0.15. If effect size f is known, plug it in to the function; If delta and sigma are known instead of effect size, put NULL to f.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with "method" and "note" elements.

Author(s)

Pengcheng Lu, Junhao Liu, and Devin Koestler.

References

Angela Dean & Daniel Voss (1999). Design and Analysis of Experiments. Springer.

Examples

## Example 1
ss.2way(a=3, b=3, alpha=0.05, beta=0.1, f.A=0.4, f.B=0.2, B=100)
ss.2way(a=3, b=3, alpha=0.05, beta=0.1, f.A=0.4, f.B=0.2, 
delta.A=NULL, delta.B=NULL, sigma.A=NULL, sigma.B=NULL, B=100)

## Example 2
ss.2way(a=3, b=3, alpha=0.05, beta=0.1, delta.A=1, delta.B=2, sigma.A=2, sigma.B=2, B=100)