Description Usage Arguments Details Value References Examples
jtrans
transforms a continuous univariate vector to a random vector
from standard normal distribution.
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x 
the nonnormal numerical data. 
test 
the normality test used to select fits, defaults to

exclude_original 
whether the original data should be excluded when comparing fits. 
z_lim 
two values vector defining the range of the z values, defaults to 0.25 to 1.25, which is recommended by Mandraccia, Halverson and Chou (1996). 
z_length 
the length of the z vector, default to 101. The number of different fits estiamted in the algorithm. Set larger z.length value if you want extra precision. 
jtrans
fits data to a set of distributions from Johnson family. A
normality test is used to find the best fit by choosing the fit with maximum
p.value under that given test. It returns the transformed data, the
corresponding type of Johnson curve and parameter estimations.
Since the default ShapiroWilk test can only accept sample size between 3 and
5000, one should specify another normality test in the test parameter,
generally the ad.test
in the nortest package is
recommended.
Sometimes, this algorithm may return poor fits. The most extreme case is that
all the transformed data have smaller p.values than the original data's. In
such cases, the exclude_original
flag should be set to FALSE, so
jtrans
will return the original data as the transformed data.
A list with two classes: the first one is the type of transformation
used, the same as the type
component, could be "sb", "su" or "sl";
The second one is "jtrans". The list containsthe following components:
original 
original data. 
transformed 
transformed data. 
type 
type of transformation selected. 
test 
normality test used to select transformations. 
z 
selected z value among 101 values from 0.25 to 1.25. 
eta, gamma, lambda, epsilon 
transformation parameters. 
p.value 
the maximum p.value returned by test 
Chou, Y. M., Polansky, A. M., & Mason, R. L. (1998). Transforming nonnormal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133141.
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