ci_rbod_constr_bad | R Documentation |
The Robust constrained Benefit of the Doubt function introduces additional constraints to the weight variation in the optimization procedure (Constrained Virtual Weights Restriction) allowing to restrict the importance attached to a single indicator expressed in percentage terms, ranging between a lower and an upper bound (VWR); this function, furthermore, allows to calculate the composite indicator simultaneously in presence of undesirable (bad) and desirable (good) indicators allowing to impose a preference structure (ordVWR). This function is the robust version of the ci_bod_constr_bad
: it is based on the concept of the expected minimum input function of order-m (Daraio and Simar, 2005) allowing to compare the unit under analysis against M
peers by extracting B
samples with replacement.
ci_rbod_constr_bad(x, indic_col, ngood=1, nbad=1, low_w=0, pref=NULL, M, B)
x |
A data.frame containing simple indicators. |
indic_col |
A numeric list indicating the positions of the simple indicators. |
ngood |
The number of desirable outputs; it has to be greater than 0. |
nbad |
The number of undesirable outputs; it has to be greater than 0. |
low_w |
Importance weights lower bound. |
pref |
The preference vector among indicators; For example if |
M |
The number of elements in each of the bootstrapped samples. |
B |
The number of bootstrap replicates. |
An object of class "CI". This is a list containing the following elements:
ci_rbod_constr_bad_est |
Composite indicator estimated values. |
ci_method |
Method used; for this function ci_method="rbod_constr_bad". |
ci_rbod_constr_bad_weights |
Raw weights assigned to each simple indicator. |
ci_rbod_constr_bad_target |
Indicator target values. |
Fusco E., Rogge N.
Rogge N., de Jaeger S. and Lavigne C. (2017) "Waste Performance of NUTS 2-regions in the EU: A Conditional Directional Distance Benefit-of-the-Doubt Model", Ecological Economics, vol.139, pp. 19-32.
Zanella A., Camanho A.S. and Dias T.G. (2015) "Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis", European Journal of Operational Research, vol. 245(2), pp. 517-530.
ci_bod_constr
, ci_bod_constr_bad
data(EU_2020)
indic <- c("employ_2011", "percGDP_2011", "gasemiss_2011","deprived_2011")
dat <- EU_2020[-c(10,18),indic]
# Robust BoD Constrained VWR
CI_BoD_C = ci_rbod_constr_bad(dat, ngood=2, nbad=2, low_w=0.05, pref=NULL, M=10, B=50)
CI_BoD_C$ci_rbod_constr_bad_est
# Robust BoD Constrained ordVWR
importance <- c("gasemiss_2011","percGDP_2011","employ_2011")
CI_BoD_C = ci_rbod_constr_bad(dat, ngood=2, nbad=2, low_w=0.05, pref=importance, M=10, B=50)
CI_BoD_C$ci_rbod_constr_bad_est
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