# effTransform: Transform effectiveness distributions towards a expected... In julian-urbano/simIReff: Stochastic Simulation for Information Retrieval Evaluation: Effectiveness Scores

## Description

Transforms the given effectiveness distribution such that its expected value matches a predefined value. For details, please refer to section 3.4 of (Urbano and Nagler, 2018).

## Usage

 ```1 2 3``` ```effTransform(eff, mean, abs.tol = 1e-05) effTransformAll(effs, means, abs.tol = 1e-05, silent = TRUE) ```

## Arguments

 `eff` the distribution to transform. `mean` the target expected value to transform to. If missing, defaults to the mean in the data used to fit `eff`, if any. `abs.tol` the absolute tolerance of the transformation. `effs` the list of distributions to transform. `means` the vector of target expected values to transform to. If missing, defaults to the means in the data used to fit `effs`, if any. `silent` logical: should the report of error messages be suppressed?

## Details

`effTransformAll` does the same but for a list of distributions and target means.

## Value

an effectiveness distribution of class `eff.cont.trans` or `eff.disc.trans`, depending on the type of distribution.

## References

J. Urbano and T. Nagler. (2018). Stochastic Simulation of Test Collections: Evaluation Scores. ACM SIGIR.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```e <- effCont_beta(web2010ap[,1]) e2 <- effTransform(e, 0.12) c(e\$mean, e2\$mean) plot(e) plot(e2) # transform a list of distributions to the observed means ee <- effContFitAndSelect(web2010ap[,1:5]) ee2 <- effTransformAll(ee) obsmeans <- colMeans(web2010ap[,1:5]) sapply(ee, function(e)e\$mean) - obsmeans sapply(ee2, function(e)e\$mean) - obsmeans ```

julian-urbano/simIReff documentation built on May 21, 2019, 9:37 a.m.