wilcox.test {ctest} | R Documentation |
Performs one and two sample Wilcoxon tests on vectors of data.
wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, exact = NULL, correct = TRUE)
x |
numeric vector of data values. |
y |
an optional numeric vector of data values. |
alternative |
the alternative hypothesis must be
one of "two.sided" (default), "greater" or
"less" . You can specify just the initial letter. |
mu |
a number specifying an optional location parameter. |
paired |
a logical indicating whether you want a paired test. |
exact |
a logical indicating whether an exact p-value should be computed. |
correct |
a logical indicating whether to apply continuity correction in the normal approximation for the p-value. |
If only x
is given, or if both x
and y
are given
and paired
is TRUE
, a Wilcoxon signed rank test of the
null that the median of x
(in the one sample case) or of
x-y
(in the paired two sample case) equals mu
is
performed.
Otherwise, if both x
and y
are given and paired
is FALSE
, a Wilcoxon rank sum test (equivalent to the
Mann-Whitney test) is carried out. In this case, the null hypothesis
is that the location of the distributions of x
and y
differ by mu
.
By default (if exact
is not specified), an exact p-value is
computed if the samples contain less than 50 finite values and there
are no ties. Otherwise, a normal approximation is used.
"htest"
containing the following components:
statistic |
the value of the test statistic with a name describing it. |
parameter |
the parameter(s) for the exact distribution of the test statistic. |
p.value |
the p-value for the test. |
null.value |
the location parameter mu . |
alternative |
a character string describing the alternative hypothesis. |
method |
the type of test applied. |
data.name |
a character string giving the names of the data. |
Myles Hollander & Douglas A. Wolfe (1973), Nonparametric statistical inference. New York: John Wiley & Sons. Pages 2733 (one-sample), 6875 (two-sample).
kruskal.test
for testing homogeneity in location
parameters in the case of two or more samples;
t.test
for a parametric alternative under normality
assumptions.
## One-sample test. ## Hollander & Wolfe (1973), 29f. ## Hamilton depression scale factor measurements in 9 patients with ## mixed anxiety and depression, taken at the first (x) and second ## (y) visit after initiation of a therapy (administration of a ## tranquilizer). x <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30) y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29) wilcox.test(x, y, paired = TRUE, alternative = "greater") wilcox.test(y - x, alternative = "less") # The same. wilcox.test(y - x, alternative = "less", exact = FALSE, correct = FALSE) # H&W large sample # approximation ## Two-sample test. ## Hollander & Wolfe (1973), 69f. ## Permeability constants of the human chorioamnion (a placental ## membrane) at term (x) and between 12 to 26 weeks gestational ## age (y). The alternative of interest is greater permeability ## of the human chorioamnion for the term pregnancy. x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46) y <- c(1.15, 0.88, 0.90, 0.74, 1.21) wilcox.test(x, y, alternative = "g")# greater wilcox.test(x, y, alternative = "greater", exact = FALSE, correct = FALSE) # H&W large sample # approximation