Weighted quantile estimator
Computes weighted quantiles (code from Andrey Akinshin (2023) "Weighted quantile estimators" arXiv:2304.07265 [stat.ME] Code made available via the CC BY-NC-SA 4.0 license) on the basis of either the weighted Harrell-Davis quantile estimator or an adaption of the type 7 quantile estimator of the generic quantile function in the base package. Please provide a vector with raw values, the probabilities for the quantiles and an additional vector with the weight of each observation. In case the weight vector is NULL, a normal quantile estimation is done. The vectors may not include NAs and the weights should be positive non-zero values. Please draw on the computeWeights() function for retrieving weights in post stratification.
weighted.quantile(x, probs, weights = NULL, type = "Harrell-Davis")
x: A numerical vectorprobs: Numerical vector of quantilesweights: A numerical vector with weights; should have the same length as xtype: Type of estimator, can either be "inflation", "Harrell-Davis" using a beta function to approximate the weighted percentiles (Harrell & Davis, 1982) or "Type7" (default; Hyndman & Fan, 1996), an adaption of the generic quantile function in R, including weighting. The inflation procedure is essentially a numerical, non-parametric solution that gives the same results as Harrel-Davis. It requires less ressources with small datasets and always finds a solution (e. g. 1000 cases with weights between 1 and 10). If it becomes too resource intense, it switches to Harrell-Davis automatically. Harrel-Davis and Type7 code is based on the work of Akinshin (2023).the weighted quantiles
weighted.quantile.inflation, weighted.quantile.harrell.davis, weighted.quantile.type7
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