A continuous distribution on the real line. For binary outcomes
the model given by \(P(Y = 1 | X) = F(X \beta)\) where
\(F\) is the Logistic cdf()
is called logistic regression.
dist_logistic(location, scale)
location and scale parameters.
We recommend reading this documentation on https://pkg.mitchelloharawild.com/distributional/, where the math will render nicely.
In the following, let \(X\) be a Logistic random variable with
location
= \(\mu\) and scale
= \(s\).
Support: \(R\), the set of all real numbers
Mean: \(\mu\)
Variance: \(s^2 \pi^2 / 3\)
Probability density function (p.d.f):
$$ f(x) = \frac{e^{-(\frac{x - \mu}{s})}}{s [1 + \exp(-(\frac{x - \mu}{s})) ]^2} $$
Cumulative distribution function (c.d.f):
$$ F(t) = \frac{1}{1 + e^{-(\frac{t - \mu}{s})}} $$
Moment generating function (m.g.f):
$$ E(e^{tX}) = e^{\mu t} \beta(1 - st, 1 + st) $$
where \(\beta(x, y)\) is the Beta function.
dist <- dist_logistic(location = c(5,9,9,6,2), scale = c(2,3,4,2,1))
dist
#> <distribution[5]>
#> [1] Logistic(5, 2) Logistic(9, 3) Logistic(9, 4) Logistic(6, 2) Logistic(2, 1)
mean(dist)
#> [1] 5 9 9 6 2
variance(dist)
#> [1] 13.159473 29.608813 52.637890 13.159473 3.289868
skewness(dist)
#> [1] 0 0 0 0 0
kurtosis(dist)
#> [1] 1.2 1.2 1.2 1.2 1.2
generate(dist, 10)
#> [[1]]
#> [1] 6.188569 5.710968 11.304809 1.800018 2.778864 5.166084 1.250027
#> [8] 7.978300 3.379552 3.730710
#>
#> [[2]]
#> [1] 6.798999 13.756778 4.238441 6.139690 7.325568 1.235022 10.080707
#> [8] 1.837710 6.105048 2.029825
#>
#> [[3]]
#> [1] 18.450425 4.181901 21.671779 5.969050 7.885592 18.572442 15.697711
#> [8] 21.040903 5.867102 16.984272
#>
#> [[4]]
#> [1] 4.300495 5.524305 5.563913 5.431653 9.654409 8.638391 4.903154
#> [8] 4.662528 12.460462 10.127657
#>
#> [[5]]
#> [1] 1.7256570 4.1173580 0.5553411 3.1175891 0.5285271 1.9572341 0.1192819
#> [8] 2.4961750 1.3551942 2.3998593
#>
density(dist, 2)
#> [1] 0.07457323 0.02686172 0.03153231 0.05249679 0.25000000
density(dist, 2, log = TRUE)
#> [1] -2.595974 -3.617053 -3.456743 -2.947003 -1.386294
cdf(dist, 4)
#> [1] 0.3775407 0.1588691 0.2227001 0.2689414 0.8807971
quantile(dist, 0.7)
#> [1] 6.694596 11.541894 12.389191 7.694596 2.847298