README.md

In Dyang11/binomial: Binomial Functions and analysis

Binomial

Overview:

The Binomial pacakge provides functions to calculate and visualize binomial distirbutions.

A binomial distritbution can be generated with:

• n trials
• p probability

Basic summary functions calculate statistics of a generated binomial distribution:

• bin_mean(trials,probability)
• bin_variance(n,p)
• bin_mode(n,p)
• bin_skewness(n,p)
• bin_kurtosis(n,p)

More complex summary funtions calculate the binomial distribution and the probability of successes:

• bin_variable(trials,probability)
• bin_choose(trials,successes)
• bin_probability(success,trials,probability)
• bin_distribution(trials,probability)
• bin_cumulative(trials,probability)

To learn about these functions, read the vignette provided or call ?bin_variable.

Installation:

Install the development version from GitHub via the package "devtools":

# Load helper detools:
install.packages("devtools") 
# To load Binomial (without vignettes):
devtools::install_github("Dyang11/binomial")

To learn about the functions, install Binomail with vignettes:

devtools::install_github("Dyang11/binomial", build_vignettes = TRUE)

Usage:

Let us supposed we are simulating 10 coin tosses, where a "success" is a heads flip.

library(Binomial)

#Set variable amounts
trials <- 10
probability <- 0.5

bin_mean(trials,probability)

## [1] 5

bin_variable(trials,probability)

## "Binomial Varaible"
## 
## Parameters
## - number of trials:  10 
## - prob of success :  0.5

summary(bin_variable(trials,probability))

## "Summary Binomial"
## 
## Parameters
## - number of trials: 10
## - prob of success : 0.5 
## 
## Measures
## - mean    : 5
## - variance: 2.5 
## - mode(s) : 5
## - skewness: 0 
## - kurtosis: -0.2

bin_distribution(trials,probability)

##    successes         prob
## 1          0 0.0009765625
## 2          1 0.0097656250
## 3          2 0.0439453125
## 4          3 0.1171875000
## 5          4 0.2050781250
## 6          5 0.2460937500
## 7          6 0.2050781250
## 8          7 0.1171875000
## 9          8 0.0439453125
## 10         9 0.0097656250
## 11        10 0.0009765625

plot(bin_distribution(trials,probability))

bin_cumulative(trials,probability)

##    successes         prob   cumulative
## 1          0 0.0009765625 0.0009765625
## 2          1 0.0097656250 0.0107421875
## 3          2 0.0439453125 0.0546875000
## 4          3 0.1171875000 0.1718750000
## 5          4 0.2050781250 0.3769531250
## 6          5 0.2460937500 0.6230468750
## 7          6 0.2050781250 0.8281250000
## 8          7 0.1171875000 0.9453125000
## 9          8 0.0439453125 0.9892578125
## 10         9 0.0097656250 0.9990234375
## 11        10 0.0009765625 1.0000000000

plot(bin_cumulative(trials,probability))

Created by Daniel Yang for Stat 133, Spring 2019. 05/03/19

Dyang11/binomial documentation built on June 1, 2019, 4:56 a.m.