data_outliers: Outlier identification

In gamlss.ggplots: Plotting Generalised Additive Model for Location, Scale and Shape

Outlier identification

Description

Those two function idententify outliers in variables oor data

Usage

data_outliers(data, value = 4, min.distinct = 50, family = SHASHo)

y_outliers(var, value = 4, family = SHASH)

Arguments

data	a data frame
var	a continues variable
value	max value from which the absolute value of the z-scores should be greater to identify outliers
min.distinct	if a variable has less distinct values than min.distinct is excluded
family	the distribution family used for standardization

Details

the continuous variables are power transforemed and then standartised

Value

return a list

Author(s)

Mikis Stasinopoulos

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

See Also

data_names

Examples

da <- rent99[,-2]
data_outliers(da)

gamlss.ggplots documentation built on Sept. 3, 2023, 5:08 p.m.