
Last chance! 50% off unlimited learning
Sale ends in
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).
effTransform(eff, mean, abs.tol = 1e-05)effTransformAll(effs, means, abs.tol = 1e-05, silent = TRUE)
the distribution to transform.
the target expected value to transform to. If missing, defaults to the mean in the
data used to fit eff
, if any.
the absolute tolerance of the transformation.
the list of distributions to transform.
the vector of target expected values to transform to. If missing, defaults to the
means in the data used to fit effs
, if any.
logical: should the report of error messages be suppressed?
an effectiveness distribution of class eff.cont.trans
or eff.disc.trans
,
depending on the type of distribution.
effTransformAll
does the same but for a list of distributions and target means.
J. Urbano and T. Nagler. (2018). Stochastic Simulation of Test Collections: Evaluation Scores. ACM SIGIR.
# NOT RUN {
e <- effCont_beta(web2010ap[,1])
e2 <- effTransform(e, 0.12)
c(e$mean, e2$mean)
plot(e)
plot(e2)
# }
# NOT RUN {
# 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
# }
Run the code above in your browser using DataLab