est.density.minq2max: Estimating Unknown Parameters using Minimum, Median and...

In metaquant: Estimating Means, Standard Deviations and Visualising Distributions using Quantiles

Estimating Unknown Parameters using Minimum, Median and Maximum

Description

This function provide estimates for the parameters of skew logistic distribution (SLD), the sample mean and the standard deviation using 3-number summary {minimum, median (q_2), maximum} from a study with sample size n, using the method explained in De Livera et al. (2024).

Usage

est.density.minq2max(
   min = NULL, 
   med = NULL, 
   max = NULL, 
   n = NULL, 
   opt = TRUE
   )

Arguments

min	numeric value representing the sample minimum.
med	numeric value representing the median of the sample.
max	numeric value representing the sample maximum.
n	numeric value specifying the sample size.
opt	logical value indicating whether to apply the optimisation step in estimating parameters using theoretical quantiles. The default value is TRUE.

Details

De Livera et al., (2024) proposed using the skew logistic distribution (SLD) to estimate unknown parameters for studies reporting 3-number summaries in the meta-analysis context.

The quantile-based skew logistic distribution, introduced by Gilchrist (2000) and further modified by van Staden and King (2015) is used to approximate the sample's distribution using 3-point summaries. The SLD quantile function is defined using three parameters: a location parameter (\lambda), a scale parameter (\eta), and a skewing parameter (\delta). The parameters of the SLD are estimated by formulating and solving a set of simultaneous equations which relate the estimated sample quantiles to their theoretical counterparts of the SLD.

Value

• parameters: named numeric vector representing the estimated parameters ('location', 'scale', 'skewing') of SLD.

• mean: numeric value of the estimated mean of the sample using SLD.

• sd: numeric value of the estimated standard deviation of the sample using SLD.

References

Alysha De Livera, Luke Prendergast, and Udara Kumaranathunga. A novel density-based approach for estimating unknown means, distribution visualisations, and meta-analyses of quantiles, 2024. Pre-print available here: https://arxiv.org/abs/2411.10971.

Warren Gilchrist. Statistical modelling with quantile functions. Chapman and Hall/CRC, 2000.

P. J. van Staden and R. A. R. King. The quantile-based skew logistic distribution. Statistics & Probability Letters, 96:109–116, 2015.

R. King and P. van Staden. sld: Estimation and Use of the Quantile-Based Skew Logistic Distribution. R package version 1.0.1, 2022. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/CRAN.package.sld")}. https://CRAN.R-project.org/package=sld.

See Also

est.density.five(), est.density.q1q2q3()

Examples

#Generate 3-number summary data
set.seed(123)
n <- 1000
x <- stats::rlnorm(n, 4, 0.3)
quants <- c(min(x), stats::quantile(x, probs = 0.5), max(x))

#Estimate SLD parameters using 3-number summary
params <- est.density.minq2max(min = quants[1], med = quants[2], max = quants[3], 
                               n=n, opt=TRUE)$parameters
params

metaquant documentation built on Aug. 21, 2025, 6:03 p.m.