Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/density_conditional.R
Conditional density of a (zero-truncated) Poisson variable Y given X=x for a Gamma-distributed variable X.
1 | density_conditional(y,x, mu, delta, lambda, theta, family,zt)
|
y |
vector at which the conditional density is evaluated |
x |
conditioning value of the Gamma distributed variable |
mu |
expectation of the Gamma distribution |
delta |
dispersion parameter of the Gamma distribution |
lambda |
parameter of the zero-truncated Poisson distribution |
theta |
copula parameter |
family |
an integer defining the bivariate copula family: 1 = Gauss, 3 = Clayton, 4=Gumbel, 5=Frank |
zt |
logical. If |
For a Gamma distributed variable X and a (zero truncated) Possion variable Y with joint density function f_{XY}(x,y), this function evaluates
P(Y=y|X=x)=\frac{f_{XY}(x,y)}{f_X(x)}\,.
The joint density function is determined by a copula famila family
with copula parameter theta
.
vector of length length(y)
Nicole Kraemer, Daniel Silvestrini
N. Kraemer, E. Brechmann, D. Silvestrini, C. Czado (2013): Total loss estimation using copula-based regression models. Insurance: Mathematics and Economics 53 (3), 829 - 839.
1 2 3 | out<-density_conditional(y=0:10,x=3,mu=1,delta=1,lambda=2,theta=0.5,family=1)
names(out)=0:10
barplot(out)
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