In SophieSXR/Workout03: Binomial Distribution

knitr::opts_chunk$set(echo = TRUE)

Overview

binomial is a package that provides functions for bino mial distributions.

bin_choose() calculates the number of combinations in which $k$ successes can occur in $n$ trials.
bin_probability() calculates the probability in which $k$ successes can occur in $n$ trials with the probability of success.
bin_distribution() calculates the binomial distribution with an output as a data frame with class "bindis","data.frame".
plot() method for a binomial distribution creates a barplot.
bin_cumulative() calculates the cumulative probability of a binomial distribution. The output is a data frame with class "bincum","data.frame".
plot() function gives you a line plot of the cumulative distribution.
bin_variable() gives the binomial variable with class "binvar". It prints out the parameters.
summary() functions gives you a summary of the variable: mean, variable, mode, skewness, kurtosis.
bin_mean() calculates mean of binomial distributions.
bin_variance() calculates variance of binomial.
bin_mode() calculates mode of binomial distributions.
bin_skewness() calculates skewness of binomial distributions.
bin_kurtosis() calculates kurtosis of binomial distributions

Motivation

This package has been developed for the model of binomial distribution.

Usage

library(binomial)

bin_choose(n = 5, k = 2)

# probability of getting 2 successes in 5 trials
# (assuming prob of success = 0.5) 
bin_probability(success = 2, trials = 5, prob = 0.5)

bin_distribution(trials=5, prob = 0.5)

dis1 <- bin_distribution(trials = 5, prob = 0.5)
plot(dis1)

bin_cumulative(trials=5, prob = 0.5)

dis2 <- bin_cumulative(trials = 5, prob = 0.5)
plot(dis2)

bin_variable(trials = 10, p = 0.3)

bin1 <- bin_variable(trials = 10, p = 0.3)
binsum1 <- summary(bin1)
binsum1

bin_mean(trials = 10, prob = 0.3)

bin_variance(trials = 10, prob = 0.3)

bin_mode(trials = 10, prob = 0.3)

bin_skewness(trials = 10, prob = 0.3)

bin_kurtosis(trials = 10, prob = 0.3)