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Logistic distribution logarithm of cumulative distribution function.
The cumulative distribution function for a logistic random variable is
where mu
is the location parameter and s > 0
is the scale parameter.
npm install @stdlib/stats-base-dists-logistic-logcdf
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
branch (see README). - If you are using Deno, visit the
deno
branch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umd
branch (see README).
The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
var logcdf = require( '@stdlib/stats-base-dists-logistic-logcdf' );
Evaluates the logarithm of the cumulative distribution function (CDF) for a logistic distribution with parameters mu
(location parameter) and s
(scale parameter).
var y = logcdf( 2.0, 0.0, 1.0 );
// returns ~-0.127
y = logcdf( 0.0, 0.0, 1.0 );
// returns ~-0.693
y = logcdf( -1.0, 4.0, 2.0 );
// returns ~-2.579
If provided NaN
as any argument, the function returns NaN
.
var y = logcdf( NaN, 0.0, 1.0 );
// returns NaN
y = logcdf( 0.0, NaN, 1.0 );
// returns NaN
y = logcdf( 0.0, 0.0, NaN );
// returns NaN
If provided s < 0
, the function returns NaN
.
var y = logcdf( 2.0, 0.0, -1.0 );
// returns NaN
If provided s = 0
, the function evaluates the logarithm of the CDF of a degenerate distribution centered at mu
.
var y = logcdf( 2.0, 8.0, 0.0 );
// returns -Infinity
y = logcdf( 8.0, 8.0, 0.0 );
// returns 0.0
y = logcdf( 10.0, 8.0, 0.0 );
// returns 0.0
Returns a function for evaluating the logarithm of the cumulative distribution function of a logistic distribution with parameters mu
(location parameter) and s
(scale parameter).
var mylogcdf = logcdf.factory( 10.0, 2.0 );
var y = mylogcdf( 10.0 );
// returns ~-0.693
y = mylogcdf( 8.0 );
// returns ~-1.313
- In virtually all cases, using the
logpdf
orlogcdf
functions is preferable to manually computing the logarithm of thepdf
orcdf
, respectively, since the latter is prone to overflow and underflow.
var randu = require( '@stdlib/random-base-randu' );
var logcdf = require( '@stdlib/stats-base-dists-logistic-logcdf' );
var mu;
var s;
var x;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
x = randu() * 10.0;
mu = randu() * 10.0;
s = randu() * 10.0;
y = logcdf( x, mu, s );
console.log( 'x: %d, µ: %d, s: %d, ln(F(x;µ,s)): %d', x, mu, s, y );
}
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