Description Usage Arguments Value References Examples
The normality
function is used to perform normality tests on a set of variables in a data.frame
.
This can include by group normality tests when including a vector of categorical variables in the groupby= argument.
The Shapiro-Wilk Test for normality will be performed in all cases. If the sample size of all groups >= 20,
then the D'Agostino Omnibus normality test will also be performed.
1 |
data |
a |
vars |
a |
groupby |
an optional |
energy |
a TRUE/FALSE boolean value indicating whether or not to perform the energy test (Székely and Rizzo) with bootstrap p-values. |
R |
the number of boostrapped resamples. Unused if energy=FALSE. |
A list
containing normality tests results for each variable listed in the vars
statement.
Szekely, G. J. and Rizzo, M. L. (2005) A New Test for Multivariate Normality, Journal of Multivariate Analysis, 93/1, 58-80, http://dx.doi.org/10.1016/j.jmva.2003.12.002.
Rizzo, M. L. (2002). A New Rotation Invariant Goodness-of-Fit Test, Ph.D. dissertation, Bowling Green State University.
J. P. Imhof (1961). Computing the Distribution of Quadratic Forms in Normal Variables, Biometrika, Volume 48, Issue 3/4, 419-426.
D'Agostino R.B., Pearson E.S. (1973); Tests for Departure from Normality, Biometrika 60, 613–22.
D'Agostino R.B., Rosman B. (1974); The Power of Geary's Test of Normality, Biometrika 61, 181–84.
Shapiro S.S., Francia R.S. (1972); An Approximate Analysis of Variance Test for Normality, JASA 67, 215–216.
Shapiro S.S., Wilk M.B., Chen V. (1968); A Comparative Study of Various Tests for Normality, JASA 63, 1343–72.
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