index_lp {agop} | R Documentation |
The l_p-index
Description
Given a sequence of non-negative numbers
,
where
for
,
the
-index for
equals to
if , or
otherwise.
Note that if
, then
where is the index proposed in (Kosmulski, 2007),
see
index_maxprod
.
Moreover, this index corresponds to the Shilkret integral
of w.r.t. some monotone measure,
cf. (Gagolewski, Debski, Nowakiewicz, 2013).
For the definition of the -index for
we refer
to (Gagolewski, Grzegorzewski, 2009a).
Usage
index_lp(x, p = Inf, projection = prod)
index.lp(x, p = Inf, projection = prod) # deprecated alias
Arguments
x |
a non-negative numeric vector |
p |
index order, |
projection |
function |
Details
The -index, by definition, is not an impact function, as
it produces 2 numeric values. Thus, it should be projected to one dimension.
However, you may set the
projection
argument
to identity
so as to obtain the 2-dimensional index
If a non-increasingly sorted vector is given, the function has O(n) run-time
for any , see (Gagolewski, Debski, Nowakiewicz, 2013).
For historical reasons, this function is also available via an alias,
index.lp
[but its usage is deprecated].
Value
result of projection
(c
())
References
Gagolewski M., Grzegorzewski P., A geometric approach to the construction of scientific impact indices, Scientometrics 81(3), 2009a, pp. 617-634.
Gagolewski M., Debski M., Nowakiewicz M., Efficient Algorithm for Computing Certain Graph-Based Monotone Integrals: the lp-Indices, In: Mesiar R., Bacigal T. (Eds.), Proc. Uncertainty Modelling, STU Bratislava, ISBN:978-80-227-4067-8, 2013, pp. 17-23.
Kosmulski M., MAXPROD - A new index for assessment of the scientific output of an individual, and a comparison with the h-index, Cybermetrics 11(1), 2007.
Shilkret, N., Maxitive measure and integration, Indag. Math. 33, 1971, pp. 109-116.
See Also
Other impact_functions:
index_g()
,
index_h()
,
index_maxprod()
,
index_rp()
,
index_w()
,
pord_weakdom()
Examples
x <- runif(100, 0, 100)
index.lp(x, Inf, identity) # two-dimensional value, can not be used
# directly in the analysis
index.lp(x, Inf, prod) # the MAXPROD-index (one-dimensional) [default]