R quantile

Compute empirical quantiles of sample data (e.g. survey data) corresponding to selected probabilities. Include the 9 methods described by (Hyndman and Fan, 1996) + the one by (Cunnane, 1978) + the one by (Filiben, 1975).

q <- quantile(x, probs, na_rm = False, type = 7, method='DIRECT', limit=(0,1))

Arguments

• x : a numeric vector or a value (character or integer) providing with the sample data; when data is not null, x provides with the name (char) or the position (int) of the variable of interest in the table;
• data : (_option_) input table, defined as a dataframe, whose column defined byx is used as sample data for the estimation; if passed, thenxshould be defined as a character or an integer; default:data=NULLand input sample data should be passed as numeric vector inx; *probs : (option) numeric vector giving the probabilities with values in [0,1]; default: probs=seq(0, 1, 0.25) like in original stats::quantile function;
• na.rm, names : (_option_) logical flags; ifna.rm=TRUE, any NA and NaN's are removed fromxbefore the quantiles are computed; ifnames=TRUE`, the result has a names attribute; these two flags follow exactly the original implementation of stats::quantile; default: na.rm= FALSE and names= FALSE;
• `type : (option) an integer in [1,11] used to select one of the 9 algorithms detailed in (Hyndman and Fan, 1996), alternatively the one inspired from (Cunnane, 1978) or the one in (Filiben, 1975), as in the Python scipy library; see the references for more details;
• `method : (option) choice of the implementation of the quantile estimation method; this can be either:
• "INHERIT" so that the function uses the original stats::quantile function already implemented in R; this is incompatible with type>9,
• "DIRECT" for a canonical implementation based on the direct transcription of the various quantile estimation algorithms;

default: method="DIRECT".

Returns q : a vector containing the quantile values.