DynamicDistribution (version 1.1)

DynDis: Dynamically Visualized Discrete Probability Distributions and Their Moments

Description

This function is aimed at dynamically visualizing discrete probability distributions and their moments when the parameters changed.

Usage

DynDis(name, par_matrix, total = c(100, 100, 100), choice = "cdf", 
interval = 0.05, const_par = c(NULL, NULL, NULL))

Arguments

name

A discrete probability distribution that you want to plot.

par_matrix

A matrix shows the range of the parameters. The column number of the matrix indicates the number of parameters in the distribution and the row number of the matrix is 2 for all the distributions. The first column shows the range for the first parameter, the second and the third column accordingly show the ranges for the second and third parameter in distributions. All the elements in the first row indicate the minimum for the parameters and those in the second row show the maximum ones.

total

A vector and its elements indicate the step length for parameters by order, with the default value c(100,100,100).

choice

A vector and its elements indicate the plot you want to show, with a default value "cdf".

interval

A value to show the speed of changing plots.

const_par

A vector and its elements indicate the value of the parameters that do not change.

Value

A dynamic graph which includes probability mass function graph and the 'choice' graph.

Details

For name, you can choose among Discrete Uniform('Dis_Uniform'), Bernoulli('Bernoulli'), Binomial('Binomial'), Hypergeometric('Hypergeometric'), Poisson('Poisson'), Geometric('Geometric'), Negative Binomial('Negative_Binomial'), Logarithmic Series('Logarithmic_Series'). For choice, you can choose among Cumulative Probability Function('cdf'), Mean('Mean'), Variance('Variance'), Mode('Mode'), Skewness('Skewness') and Kurtosis('Kurtosis').

More details about distributions and parameters are as follows:

Bernoulli: Bernoulli distribution. The Bernoulli probability parameter is p, 0

Binomial: Binomial distribution. The Bernoulli trial parameter is n, and the probability parameter is p, 0

Dis_Uniform: Discrete Uniform distribution. The parameter is n.

Geometric: Geometric distribution. The Geometric trial parameter is n, and the probability parameter is p, 0

Hypergeometric: Hypergeometric distribution. Parameter N: the number of elements in the population. Parameter M: the number of successes in the population. Parameter n: sample size. The order of parameters is N, M, n. See Note below.

Logarithmic_Series: Logarithmic Series Distribution. Shape parameter theta, 0

Negative_Binomial: Negative Binomial distribution. The distribution of the random variable that represents the number of failures until the rth success is called geometric distribution. Parameter r: rth success.Parameter p: the Bernoulli probability parameter, 0

Poisson: Poisson distribution. The parameter is lamda.

References

K. Krishnamoorthy(2006) Handbook of Statistical Distributions with Applications University of Louisiana at Lafayette.

Examples

DynDis(name=Negative_Binomial,par_matrix=matrix(c(1,12,0.1,0.9),2,2)
,choice='Kurtosis',const_par=c(4,0.7))

DynDis(name=Bernoulli,par_matrix=matrix(c(0.1,0.9),2,1),choice='cdf')

DynDis(name=Binomial,par_matrix=matrix(c(1,12,0.1,0.9),2,2),choice='Mean'
,const_par=c(4,0.7))

DynDis(name=Logarithmic_Series,par_matrix=matrix(c(0.1,0.9),2,1),
choice='Variance')

DynDis(name=Geometric,par_matrix=matrix(c(0.1,0.9),2,1),choice='Skewness')

DynDis(name=Hypergeometric,par_matrix=matrix(c(1,3,2,8,10,20),2,3),
choice='Kurtosis',const_par=c(4,5,6))

DynDis(name=Dis_Uniform,par_matrix=matrix(c(2,5),2,1),choice='Skewness')

DynDis(name=Poisson,par_matrix=matrix(c(2,20),2,1),choice='Kurtosis')