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Type: Package
Title: Sample Size Calculation for Various t-Tests and Wilcoxon-Test
Version: 0.2-4
Date: 2016-12-22
Author: Ralph Scherer
Maintainer: Ralph Scherer <shearer.ra76@gmail.com>
Description: Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/shearer/samplesize
BugReports: https://github.com/shearer/samplesize/issues
NeedsCompilation: no
Packaged: 2016-12-23 21:02:40 UTC; ralph
Repository: CRAN
Date/Publication: 2016-12-24 11:24:04

Computes sample size for several two-sample tests

Description

Computes sample size for independent and paired Student's t-test, Student's t-test with Welch-approximation, Wilcoxon-Mann-Whitney test with and without ties on ordinal data

Details

Package: samplesize
Type: Package
Version: 0.2-4
Date: 2016-12-22
License: GPL (>=2)
LazyLoad: yes

n.ttest(): sample size for Student's t-test and t-test with Welch approximation

n.wilcox.ord(): sample size for Wilcoxon-Mann-Whitney test with and without ties

Author(s)

Ralph Scherer

Maintainer: Ralph Scherer <shearer.ra@gmail.com>

References

Bock J., Bestimmung des Stichprobenumfangs fuer biologische Experimente und kontrollierte klinische Studien. Oldenbourg 1998

Zhao YD, Rahardja D, Qu Yongming. Sample size calculation for the Wilcoxon-Mann-Whitney test adjusting for ties. Statistics in Medicine 2008; 27:462-468


n.ttest computes sample size for paired and unpaired t-tests.

Description

n.ttest computes sample size for paired and unpaired t-tests. Design may be balanced or unbalanced. Homogeneous and heterogeneous variances are allowed.

Usage

n.ttest(power = 0.8, alpha = 0.05, mean.diff = 0.8, sd1 = 0.83, sd2 = sd1,
        k = 1, design = "unpaired", fraction = "balanced", variance = "equal")

Arguments

power

Power (1 - Type-II-error)

alpha

Two-sided Type-I-error

mean.diff

Expected mean difference

sd1

Standard deviation in group 1

sd2

Standard deviation in group 2

k

Sample fraction k

design

Type of design. May be paired or unpaired

fraction

Type of fraction. May be balanced or unbalanced

variance

Type of variance. May be homo- or heterogeneous

Value

Total sample size

Sample size for both groups together

Sample size group 1

Sample size in group 1

Sample size group 2

Sample size in group 2

Author(s)

Ralph Scherer

References

Bock J., Bestimmung des Stichprobenumfangs fuer biologische Experimente und kontrollierte klinische Studien. Oldenbourg 1998

Examples

n.ttest(power = 0.8, alpha = 0.05, mean.diff = 0.80, sd1 = 0.83, k = 1,
design = "unpaired", fraction = "balanced", variance = "equal")

n.ttest(power = 0.8, alpha = 0.05, mean.diff = 0.80, sd1 = 0.83, sd2 =
2.65, k = 0.7, design = "unpaired", fraction = "unbalanced", variance =
"unequal")

Sample size for Wilcoxon-Mann-Whitney for ordinal data

Description

Function computes sample size for the two-sided Wilcoxon test when applied to two independent samples with ordered categorical responses.

Usage

n.wilcox.ord(power = 0.8, alpha = 0.05, t, p, q)

Arguments

power

required Power

alpha

required two-sided Type-I-error level

t

sample size fraction n/N, where n is sample size of group B and N is the total sample size

p

vector of expected proportions of the categories in group A, should sum to 1

q

vector of expected proportions of the categories in group B, should be of equal length as p and should sum to 1

Details

This function approximates the total sample size, N, needed for the two-sided Wilcoxon test when comparing two independent samples, A and B, when data are ordered categorical according to Equation 12 in Zhao et al.(2008). Assuming that the response consists of D ordered categories C_1 ,..., C_D. The expected proportions of these categories in two treatments A and B must be specified as numeric vectors p_1,...,p_D and q_1,...,q_D, respectively. The argument t allows to compute power for an unbalanced design, where t=n_B/N is the proportion of sample size in treatment B.

Value

total sample size

Total sample size

m

Sample size group 1

n

Sample size group 2

Author(s)

Ralph Scherer

References

Zhao YD, Rahardja D, Qu Yongming. Sample size calculation for the Wilcoxon-Mann-Whitney test adjsuting for ties. Statistics in Medicine 2008; 27:462-468

Examples

## example out of:
## Zhao YD, Rahardja D, Qu Yongming. 
## Sample size calculation for the Wilcoxon-Mann-Whitney test adjsuting for ties. 
## Statistics in Medicine 2008; 27:462-468
n.wilcox.ord(power = 0.8, alpha = 0.05, t = 0.53, p = c(0.66, 0.15, 0.19), q = c(0.61, 0.23, 0.16))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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