Geometric {distributions3} R Documentation

## Create a Geometric distribution

### Description

The Geometric distribution can be thought of as a generalization of the Bernoulli() distribution where we ask: "if I keep flipping a coin with probability p of heads, what is the probability I need k flips before I get my first heads?" The Geometric distribution is a special case of Negative Binomial distribution.

### Usage

Geometric(p = 0.5)


### Arguments

 p The success probability for the distribution. p can be any value in ⁠[0, 1]⁠, and defaults to 0.5.

### Details

We recommend reading this documentation on https://alexpghayes.github.io/distributions3/, where the math will render with additional detail and much greater clarity.

In the following, let X be a Geometric random variable with success probability p = p. Note that there are multiple parameterizations of the Geometric distribution.

Support: 0 < p < 1, x = 0, 1, \dots

Mean: \frac{1-p}{p}

Variance: \frac{1-p}{p^2}

Probability mass function (p.m.f):

 P(X = x) = p(1-p)^x, 

Cumulative distribution function (c.d.f):

 P(X \le x) = 1 - (1-p)^{x+1} 

Moment generating function (m.g.f):

 E(e^{tX}) = \frac{pe^t}{1 - (1-p)e^t} 

### Value

A Geometric object.

Other discrete distributions: Bernoulli(), Binomial(), Categorical(), HurdleNegativeBinomial(), HurdlePoisson(), HyperGeometric(), Multinomial(), NegativeBinomial(), Poisson(), ZINegativeBinomial(), ZIPoisson(), ZTNegativeBinomial(), ZTPoisson()

### Examples


set.seed(27)

X <- Geometric(0.3)
X

random(X, 10)

pdf(X, 2)
log_pdf(X, 2)

cdf(X, 4)
quantile(X, 0.7)


[Package distributions3 version 0.2.1 Index]