outliers_det_boxplot: Sequential outlier detection using Boxplot

In ICRISAT-GEMS/SpaTemHTP: Processing and utilization of HTP data

Description

Detect outliers using boxplot method (Sun and Genton, 2011). For each day, the 25% quantile (QR1), 75% quantile (QR3), and 50% inter-quantile range (IQR) are calculated. The observations below QR1 - 1.5 x IQR or above QR3 + 1.5 x IQR are considered as outliers. Outliers are replaced by missing value.

Usage

outliers_det_boxplot(data, plot = TRUE)

Arguments

data	data.frame of dimension (N_genotype * N_replicate) x N_days containing the measured phenotypic values.
plot	Logical value indicating if the boxplot of each day should be plotted. Default = FALSE.

Value

Return:

data.frame with outlying values put as NA.

Author(s)

Soumyashree Kar, Vincent Garin

References

Sun, Y. and Genton, M.G. (2011). Functional boxplots. Journal of Computational and Graphical Statistics, 20(2), pp.316-334

Examples

data(SG_PH_data)

data <- outliers_det_boxplot(data = SG_PH_data[, 6:28])

ICRISAT-GEMS/SpaTemHTP documentation built on March 9, 2021, 12:12 a.m.