DynamicDistribution (version 1.1)

DynCon: Dynamically Visualized Continuous Probability Distributions and Their Moments

Description

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

Usage

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

Arguments

name

A discrete probability distribution that you want to plot.

par_matrix

A matrix shows the ranges 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 column accordingly show the ranges for the second 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).

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 density function graph and 'choice' graph.

Details

For name, you can choose among Continuous Uniform('Con_Uniform'), Normal('Normal'), Chi-Square('Chi_Square'), F('F_dis'), Student's t('Student_t'), Exponential('Exponential'), Gamma('Gamma_dis'), Beta('Beta_dis'), Laplace('Laplace'), Logistic('Logistic'), Lognormal('Lognormal'), Pareto('Pareto'), Cauchy('Cauchy'), Inverse Gaussian('Inverse_Gaussian'), Rayleigh('Rayleigh'). 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:

Beta: Beta distribution. Shape parameters a, b, a>0, b>0.

Cauchy: Cauchy distribution. Location parameter a. Scale parameter b, b>0.The order of parameters is a, b. See Note Below.

Con_Uniform: Continuous Uniform distribution. Location parameter a, the lower bound of the range. Parameter b, the upper bound of the range. The order of parameters is a, b. See Note Below.

Chi_Square: Chi-squared Distribution. Shape parameter n, degrees of freedom.

Exponential: Exponential Distribution. The scale parameter b, b>0.

F_Dis: F(central) Distribution. Shape parameters m, n, positive integers.

Gamma: Gamma distribution. Shape parameter a, a>0.Scale parameter b, b>0.The order of parameters is a, b. See Note below.

Inverse_Gaussian: Inverse Gaussian (Wald) distribution. Scale parameter lamda, lamda>0. Location parameter mu, mu>0. The order of parameters is lamda,mu. See Note below.

Laplace: Laplace distribution. Location parameter a. Scale parameter b, b>0.The order of parameters is a, b. See Note below.

Logistic: Logistic distribution. Location parameter a, scale parameter b, b>0.The order of parameters is a, b. See Note below.

Lognormal: Lognormal distribution. Scale parameter mu, mu>0.Shape parameter sigma, sigma>0.The order of parameters is mu, sigma. See Note below.

Normal: Normal distribution. Location parameter mu. Scale parameter sigma, sigma>0. The order of parameters is mu, sigma. See Note below.

Pareto: Pareto distribution. Location parameter a, a>0.shape parameter b, b>0.The order of parameters is a, b. See Note below.

Rayleigh: Rayleigh distribution. Scale parameter b>0.

Student_t:Student's t distribution. Shape parameter n, degrees of freedom, n is a positive integer.

References

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

Examples

choice='cdf',const_par=c(0,1))

DynCon(name=Inverse_Gaussian,par_matrix=matrix(c(1,12,10,20),2,2)
,choice='Kurtosis',const_par=c(2,3))

DynCon(name=Exponential,par_matrix=matrix(c(1,20),2,1),choice=
'Skewness')

DynCon(name=Normal,par_matrix=matrix(c(1,20,10,20),2,2),choice=
'Variance',const_par=c(0,1))

DynCon(name=Logistic,par_matrix=matrix(c(1,12,10,20),2,2),choice
='Kurtosis',const_par=c(2,3))