Functions for calculating skewness and kurtosis
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either a vector of effect sizes or a data frame using the standard column names.
Kurtosis is a measure of how well a distribution matches a
Gaussian distribution. A Gaussian distribution has a kurtosis
0. Negative kurtosis indicates a flatter
distribution curve, while positive kurtosis indicates a
sharper, thinner curve.
Skewness is a measure of distribution asymmetry. A symmetrical
distribution has skewness
0. A positive skewness
indicates a long tail towards higher values, while a negative
skewness indicates a long tail towards lower values.
Kurtosis is calculated as:
sum( (ES - mean(ES))^4) / ((length(ES)-1) * sd(ES)^4 )
Skewness is calculated as:
sum( (ES - mean(ES))^3) / ((length(ES)-1) * sd(ES)^3 )
Respectively the kurtosis and skewness of the input effect-size distribution.
Both functions accept vectors as
is a data frame, the column names must match the standard
names used by
"EFF_ALL_FREQ" for allele frequency, etc.)
For plotting skewness and kurtosis:
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