CITAN (version 2011.02-1)

index.lp: The $l_p$-index

Description

Computes the $l_p$-index of a numeric vector for given $p$.

Usage

index.lp(x, p=Inf, sorted.dec=FALSE, disable.check=FALSE)

Arguments

x
a non-negative numeric vector.
p
index order, $p \in [1,\infty]$; defaults $\infty$ (Inf).
sorted.dec
logical; TRUE if the vector has already been sorted non-increasingly; defaults FALSE.
disable.check
logical; TRUE to disable some validity checks on the input vector; defaults FALSE.

Value

  • The function returns a numeric vector of length 2 equal to $(i,x_i)$ or NA if improper input has been given.

Details

Given a sequence of $n$ non-negative numbers $x=(x_1,\dots,x_n)$, where $x_i \ge x_j$ for $i \le j$, the $l_p$-index for $p=\infty$ equals to $$l_p(x)=\arg\max_{(i,x_i), i=1,\dots,n} { i x_i }$$ if $n \ge 1$, or $l_\infty(x)=0$ otherwise. Note that if $(i,x_i)=l_\infty(x)$, then $$MAXPROD(x) = i x_i,$$ where $MAXPROD$ is the index proposed in (Kosmulski, 2007).

For the definition of the $l_p$-index for $p < \infty$ we refer to (Gagolewski, Grzegorzewski, 2009a).

If disable.check is set to FALSE, then eventual NA values are removed from the input vector.

If a non-increasingly sorted vector is given as input (set sorted.dec to TRUE) the result is computed in linear time (see Gagolewski, Debski, Nowakiewicz, 2009b).

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 algorithms for computing ''geometric'' scientific impact indices, Research Report of Systems Research Institute, Polish Academy of Sciences RB/1/2009, 2009b. 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.

See Also

index.h, index.g, index.rp, Sstat, Sstat2

Examples

Run this code
x <- runif(100, 0, 100);
index.lp(x);                # two-dimensional value, can not be used
# directly in the analysis
prod(index.lp(x));          # the MAXPROD-index (one-dimensional)
mean(index.lp(x,1));        # some other one-dimensional impact index}

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