Description Usage Arguments Details Value Note See Also Examples

View source: R/calc_kurtosis.R

Functions for calculating skewness and kurtosis

1 2 3 4 5 6 |

`input` |
either a vector of effect sizes or a data frame using the standard column names. |

`FRQ_val, HWE_val, cal_val, imp_val, ...` |
arguments
passed to |

Kurtosis is a measure of how well a distribution matches a
Gaussian distribution. A Gaussian distribution has a kurtosis
of `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 `input`

. If `input`

is a data frame, the column names must match the standard
names used by `QC_GWAS`

(`"EFFECT"`

for
effect sizes, `"EFF_ALL_FREQ"`

for allele frequency, etc.)

For plotting skewness and kurtosis:
`plot_skewness`

.

1 2 3 4 5 6 7 8 | ```
data("gwa_sample")
calc_kurtosis(gwa_sample$EFFECT)
calc_kurtosis(gwa_sample)
calc_kurtosis(gwa_sample$EFF_ALL_FREQ)
calc_kurtosis(gwa_sample,
FRQ_val = 0.05, cal_val = 0.95,
filter_NA = FALSE)
``` |

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