| sim.ssize.wilcox.test {MKpower} | R Documentation |
Sample Size for Wilcoxon Rank Sum and Signed Rank Tests
Description
Simulate the empirical power of Wilcoxon rank sum and signed rank tests for computing the required sample size.
Usage
sim.ssize.wilcox.test(rx, ry = NULL, mu = 0, sig.level = 0.05, power = 0.8,
type = c("two.sample", "one.sample", "paired"),
alternative = c("two.sided", "less", "greater"),
n.min = 10, n.max = 200, step.size = 10,
iter = 10000, BREAK = TRUE, exact = NA, correct = TRUE)
Arguments
rx |
function to simulate the values of x, respectively x-y in the paired case. |
ry |
function to simulate the values of y in the two-sample case |
mu |
true values of the location shift for the null hypothesis. |
sig.level |
significance level (Type I error probability) |
power |
two-sample, one-sample or paired test |
type |
one- or two-sided test. Can be abbreviated. |
alternative |
one- or two-sided test. Can be abbreviated. |
n.min |
integer, start value of grid search. |
n.max |
integer, stop value of grid search. |
step.size |
integer, step size used in the grid search. |
iter |
integer, number of interations of the simulations. |
BREAK |
logical, grid search stops when the emperical power is larger than the requested power. |
exact |
logical or NA (default) indicator whether an exact p-value should
be computed (see Details at |
correct |
ogical indicator whether continuity correction should be applied
in the cases where p-values are obtained using normal approximation. A single
value or logical vector with values for each observation; see
|
Details
Functions rx and ry are used to simulate the data and
functions row_wilcoxon_twosample and row_wilcoxon_onesample of
package matrixTests are used to efficiently compute the p values of the
respective test.
We recommend a two steps procedure: In the first step, start with a wide grid
and find out in which range of sample size values the intended power will
be achieved. In the second step, the interval identified in the first step
is used to find the sample size that leads to the required power setting
step.size = 1 and BREAK = FALSE. This approach is applied
in the examples below.
Value
Object of class "power.htest", a list of the arguments
(including the computed one) augmented with method and
note elements.
Author(s)
Matthias Kohl Matthias.Kohl@stamats.de
References
Wilcoxon, F (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1, 80-83.
See Also
Examples
###############################################################################
## two-sample
## iter = 1000 to reduce check time
###############################################################################
rx <- function(n) rnorm(n, mean = 0, sd = 1)
ry <- function(n) rnorm(n, mean = 0.5, sd = 1)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.max = 100, iter = 1000)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.min = 65, n.max = 70, step.size = 1,
iter = 1000, BREAK = FALSE)
## compared to
power.t.test(delta = 0.5, power = 0.8)
rx <- function(n) rnorm(n, mean = 0, sd = 1)
ry <- function(n) rnorm(n, mean = 0.5, sd = 1.5)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.max = 100, iter = 1000, alternative = "less")
sim.ssize.wilcox.test(rx = rx, ry = ry, n.min = 85, n.max = 90, step.size = 1,
iter = 1000, BREAK = FALSE, alternative = "less")
## compared to
power.welch.t.test(delta = 0.5, sd = 1, sd2 = 1.5, power = 0.8, alternative = "one.sided")
rx <- function(n) rnorm(n, mean = 0.5, sd = 1)
ry <- function(n) rnorm(n, mean = 0, sd = 1)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.max = 100, iter = 1000, alternative = "greater")
sim.ssize.wilcox.test(rx = rx, ry = ry, n.min = 50, n.max = 55, step.size = 1,
iter = 1000, BREAK = FALSE, alternative = "greater")
## compared to
power.t.test(delta = 0.5, power = 0.8, alternative = "one.sided")
rx <- function(n) rgamma(n, scale = 10, shape = 1)
ry <- function(n) rgamma(n, scale = 15, shape = 1)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.max = 200, iter = 1000)
sim.ssize.wilcox.test(rx = rx, ry = ry, n.min = 125, n.max = 135, step.size = 1,
iter = 1000, BREAK = FALSE)
###############################################################################
## one-sample
## iter = 1000 to reduce check time
###############################################################################
rx <- function(n) rnorm(n, mean = 0.5, sd = 1)
sim.ssize.wilcox.test(rx = rx, mu = 0, type = "one.sample", n.max = 100, iter = 1000)
sim.ssize.wilcox.test(rx = rx, mu = 0, type = "one.sample", n.min = 33, n.max = 38,
step.size = 1, iter = 1000, BREAK = FALSE)
## compared to
power.t.test(delta = 0.5, power = 0.8, type = "one.sample")
sim.ssize.wilcox.test(rx = rx, mu = 0, type = "one.sample", n.max = 100, iter = 1000,
alternative = "greater")
sim.ssize.wilcox.test(rx = rx, mu = 0, type = "one.sample", n.min = 25, n.max = 30,
step.size = 1, iter = 1000, BREAK = FALSE, alternative = "greater")
## compared to
power.t.test(delta = 0.5, power = 0.8, type = "one.sample", alternative = "one.sided")
sim.ssize.wilcox.test(rx = rx, mu = 1, type = "one.sample", n.max = 100, iter = 1000,
alternative = "less")
sim.ssize.wilcox.test(rx = rx, mu = 1, type = "one.sample", n.min = 20, n.max = 30,
step.size = 1, iter = 1000, BREAK = FALSE, alternative = "less")
## compared to
power.t.test(delta = 0.5, power = 0.8, type = "one.sample", alternative = "one.sided")
rx <- function(n) rgamma(n, scale = 10, shape = 1)
sim.ssize.wilcox.test(rx = rx, mu = 5, type = "one.sample", n.max = 200, iter = 1000)
sim.ssize.wilcox.test(rx = rx, mu = 5, type = "one.sample", n.min = 40, n.max = 50,
step.size = 1, iter = 1000, BREAK = FALSE)
###############################################################################
## paired
## identical to one-sample, requires random number generating function
## that simulates the difference x-y
## iter = 1000 to reduce check time
###############################################################################
rxy <- function(n) rnorm(n, mean = 0.5, sd = 1)
sim.ssize.wilcox.test(rx = rxy, mu = 0, type = "paired", n.max = 100,
iter = 1000)
sim.ssize.wilcox.test(rx = rxy, mu = 0, type = "paired", n.min = 33,
n.max = 38, step.size = 1, iter = 1000, BREAK = FALSE)
## compared to
power.t.test(delta = 0.5, power = 0.8, type = "paired")