| extractQuantilesDS2 {dsBase} | R Documentation |
identify the global values of V2BR (i.e. the values across all studies) that relate to a set of quantiles to be evaluated.
extractQuantilesDS2(extract.summary.output.ranks.df)
extract.summary.output.ranks.df |
character string specifies an optional name for the data.frame written to the serverside on each data source that contains 5 of the key output variables from the ranking procedure pertaining to that particular data source. This data frame represents the key source of information - including global ranks - that determines the values of V2BR that are identified as corresponding to the particular set of quantiles to be estimated as specified by the <quantiles.for.estimation> argument of function ds.ranksSecure (and the <extract.quantiles> argument of ds.extractQuantiles). |
Severside aggregate function called by ds.extractQuantiles via ds.ranksSecure. This takes the "global.bounds.df" data frame saved on the serverside following construction by extractQuantilesDS1. This data frame includes the two quantile values that most closely span each quartile value to be estimated. If either of the values had been the correct value for a given quantile, both the bounding values would have taken that value in global.bounds.df. This is because the upper bound was defined as the lowest value that was equal to or greater than the true value for that quantile while the lower bound was defined as the highest value that was equal to or lower than the true value. Next, the function extractQuantileDS2 goes round study by study to identify the values of V2BR that actually correspond to each of the spanning values around each quantile. Then the function goes quantile by quantile and estimates the mean of the two values of V2BR that correspond to the the spanning quantiles. If these two values are the same it means that that value of V2BR is the "true" value and the mean of two (or potentially several) instances of that value is inevitably also equal to that true value. If the upper and lower bounding values of V2BR differ, neither can be the precisely correct single value of V2BR for that quantile (see above for explanation) and so the mean of the two is a reasonable interpolated summary.
the single value of V2BR which best corresponds to each key quantile value to be estimated as specified by the argument <quantiles.for.estimation> A data frame (final.quantile.df)summarising the results of this analysis is written to the clientside. This data frame consists of two vectors. The first is named "evaluation.quantiles". It lists the full set of quantiles you have requested for evaluation as specified by the argument "quantiles.for.estimation" The second vector which is called "final.quantile.vector" details the values of V2BR that correspond to the the key quantiles listed in vector 1.
Paul Burton 11th November, 2021