sample_Mardia_measures | R Documentation |
Given a multivariate sample, the Mardia measures of skewness and kurtosis are computed, along with their p-values for testing normality
sample_Mardia_measures(data, correct = FALSE)
data |
a data matrix |
correct |
(logical) if |
For a given a data matrix, the multivariate measures of skewness and kurtosis introduced by Mardia (1970, 1974) are computed, along with some associated quantities. We follow the notation of the 1974 paper.
If n
denotes the number of complete cases, the condition n>3
is required for numerical computation. Clearly, a much larger n
is
required for meaningful statistical work.
The sample variance matrix S appearing in (2.2) and (2.4)
is computed here (in the dafault setting) with the n denominator,
at variance from the commonly employed n-1
denominator.
With this definition of S, one obtains the same numerical outcome
of the example on p.127 of Mardia (1974).
The approximate observed significance levels for testing normality,
p.b1
and p.b2
, are computed using expressions (5.5) and
(5.6) in Section 5 of Mardia (1974).
For p.b2
, the condition (n-d-1)>0
is required, where
d
denotes the number of variables.
A named vector with the following components:
b1 |
the measure of asymmetry as given in (2.2) |
b2 |
the measure of kurtosis as given in (2.4) |
g1 |
the measure of asymmetry as given in (2.10) |
g2 |
the measure of kurtosis as given in (2.11) |
p.b1 |
observed significance level of |
p.b2 |
observed significance level of |
n |
The number of complete cases in the input data matrix |
where the quoted formulae are those of Mardia (1974).
Adelchi Azzalini
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications Biometrika, 57, 519-530.
Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya ser.B, 36, 115-128.
set.seed(1) x <- rmnorm(100, mean=1:3, varcov=toeplitz(1/(1:3))) sample_Mardia_measures(x)
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