reflimR (version 1.0.6)

iboxplot: Removal of pathological values

Description

Iteratively truncates a vector of quantitative laboratory results until no more values outside the specified truncation interval are left.

Usage

iboxplot(x, lognormal = NULL, perc.trunc = 2.5,
apply.rounding = TRUE,
plot.it = TRUE, main = "iBoxplot", xlab = "x")

Value

$trunc

truncated vector x

$limits

truncation points, preliminary reference limits

$lognormal

Boolean indicating whether a lognormal distribution has been assumed

$perc.norm

proportion of the assumed non-pathological values

$progress

results of the iterative truncation

Arguments

x

vector of positive numbers

lognormal

Boolean indicating whether a lognormal distribution should be assumed (NULL means that the distribution type is defined automatically)

perc.trunc

percentage of presumably normal values to be removed from each side

apply.rounding

Boolean indicating whether the estimated reference limits should be rounded

plot.it

Boolean indicating whether a graphic should be created

main, xlab

title and x label of the graphic

Details

The truncated vector represents the estimated central 95 percent of values, which follow the assumed distribution (normal or lognormal). If the distribution of the reference values is unknown, medical laboratory results should be assumed to be lognormally distributed [2].

References

1. Klawonn F, Hoffmann G. Using fuzzy cluster analysis to find interesting clusters. In: L.A. Garcia-Escuderoet al. (eds.): Building bridges between soft and statistical methodologies for data science. Springer, Cham (2023), 231-239. tools:::Rd_expr_doi("10.1007/978-3-031-15509-3_31").

2. Haeckel R, Wosniok W. Observed unknown distributions of clinical chemical quantities should be considered to be log-normal. Clin Chem Lab Med 2010; 48: 1393-6. tools:::Rd_expr_doi("10.1515/CCLM.2010.273").

Examples

set.seed(123)
iboxplot(rlnorm(n = 250, meanlog = 3,  sdlog = 0.3))

iboxplot(rnorm(1000, 100, 10), apply.rounding = FALSE, plot.it = FALSE)$truncation.points

alb.trunc <- iboxplot(livertests$ALB, main = "ALB", xlab = "g/L")$trunc
summary(alb.trunc)