In Thomas-Sperandeo/workout3: Binomial

knitr::opts_chunk$set(collapse = T, comment = "#>")

library(binomial)

Binomial

The package "binomial" is a minimal implementation for calculating and visualizing the probability for binomial distributions with given inputs trials, prob and success

Creating a numeric object for permutations with bin_choose()

The first step is to create a numeric object with given arguments trials and probability using bin_choose(). By default, bin_choose() automatically check that our inputs for trials and successes are valid. The returned value will be the number of permutations to achieve the specified number of successes in given amount of trials. Successes may be a vector of more than one element.

variable <- bin_choose(trials = 10, prob = 3)

variable

Calculating binomial probability using bin_probability()

We can calculate the binomial probability for given arguments trials, successes and prob with the function bin_probability(). Successes may be a vector of more than one element.

p <- bin_probability(10, 2:4, 0.8)
p

Getting distribution of binomial probabilites using bin_distribution()

We can calculate the entire distribution of binomial probabilities for successes = 0 to successes = length(trials) using bin_distribution() for given arguments trials and prob. Our function will return an object of class c("bindis", "data.frame").

dis <-bin_distribution(trials = 5, prob = 0.3)
dis
class(dis)

Plot of bin_distribution using plot.bindis()

We can plot our binomial distribution with given input arguments trials and prob using the function plot.bindis().

graph <- plot.bindis(trials = 15, prob = 0.2)
graph

Getting culmulative binomial probabilites using bin_cumulative()

In order to calculate the cumulative probablities of our binomial distribution, we must use the function bin_cumulative() which has given input arguments trials and prob. Our function will return an object of class c("bincum", "data.frame").

binc <- bin_cumulative(10, 0.5)
binc
class(binc)

Plotting our cumulative binomial distribution using plot.bincum()

We can plot our cumulative binomial distribution using the function plot.bincum() with given input arguments trials and prob.

cumulative_plot <- plot.bincum(10, 0.1)
cumulative_plot

Thomas-Sperandeo/workout3 documentation built on May 5, 2019, 12:26 a.m.