In SophieSXR/Workout03: Binomial Distribution

knitr::opts_chunk$set(
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Getting started with binomial

First, load the package.

library(binomial)

Binomial distribution is the theoretical probability model we use when calculating probabilities about the number of successes in a fixed number of random trials performed under identical conditions (assuming a constant probability of success on each trial).

Binomial choose

The first step is to calculates the number of combinations in which $k$ successes can occur in $n$ trials.

bin_choose(n = 5, k = 2)

Binomial Probability

Then, we can calculate the probability in which $k$ successes can occur in $n$ trials with the probability of success. In this process, the function takes three arguments: success, trials, and prob.

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

Binomial Distribution

When we calculate the binomial distribution, we get a data frame with class "bindis","data.frame".

bin_distribution(trials=5, prob = 0.5)

The plot() function gives you a barplot of the distribution:

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

Binomial Cumulative

bin_cumulative() calculates the cumulative probability of a binomial distribution. The output is a data frame with class "bincum","data.frame".

bin_cumulative(trials=5, prob = 0.5)

The plot() function gives you a line plot of the cumulative distribution:

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

Binomial Variable

bin_variable() gives the binomial variable with class "binvar". It prints out the parameters.

bin_variable(trials = 10, p = 0.3)

The summary() functions gives you a summary of the variable: mean, variable, mode, skewness, kurtosis. It is printed out nicely:

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

Measures

bin_mean() calculates mean of binomial distributions:

bin_mean(trials = 10, prob = 0.3)

bin_variance() calculates variance of binomial distributions:

bin_variance(trials = 10, prob = 0.3)

bin_mode() calculates mode of binomial distributions:

bin_mode(trials = 10, prob = 0.3)

bin_skewness() calculates skewness of binomial distributions:

bin_skewness(trials = 10, prob = 0.3)

bin_kurtosis() calculates kurtosis of binomial distributions:

bin_kurtosis(trials = 10, prob = 0.3)

SophieSXR/Workout03 documentation built on May 4, 2019, 12:54 a.m.