CondBED: Conditional distribution of Y given X=x of the bivariate...

In MDBED: Moran-Downton Bivariate Exponential Distribution

Description

This function computes the conditional PDF and CDF of Y given X=x of the BED based on the equations of the conditional moments of the Moran-Downton model.

Usage

CondBED(rho,Betax,Betay,x)

Arguments

rho	Correlation coefficient between the marginal distributions of x and y.
Betax	Scale parameter of the marginal distribution of x.
Betay	Scale parameter of the marginal distribution of y.
x	a value or vector of values of the marginal distribution of x.

Details

This function computes the conditional PDF and CDF of Y given X=x. The conditional density is computed based on the conditional moments of the Moran-Downton model described in Eqs. 21 and 22 in \insertCiteNagao1971;textualMDBED. The conditional CDF is computed via numerical integration of the conditional PDF based on the Riemann sum method.

Value

A list with a dataframe, named Conditional_Statistics, and a sublist, named Condyx, is provided. Conditional_Statistics contains the values of x with its conditional moments, and Condyx contains several dataframes with the values of the conditional PDF and CDF of each conditional distribution associated to each value of x. For each conditional distribution, the values of the conditional quantiles (yc) with its respective density values (fyx) and its associated cumulative conditional probability values (Fyx) are provided.The first dataframe of Condyx corresponds to the first value of x shown in Conditional_Statistics, the second dataframe corresponds to the second value of x shown in Conditional_Statistics and so on.

Author(s)

Luis F. Duque <[e-mail]> <[e-mail]>

References

\insertAllCited

Examples

Data<-rBED(n=50,Betax=1,Betay=1,rho=0.85)
Conyx<-CondBED(rho=0.85,Betax=1,Betay=1,x=Data[,1])

MDBED documentation built on Feb. 22, 2020, 1:11 a.m.