HeritSeq (version 1.0.2)

computeVPC.CP: Calculate the compound Poisson (CP) variance partition coefficient (VPC) for one or more features.

Description

Calculate the CP VPC for one or more features following the model fitting function fit.CP().

Usage

computeVPC.CP(para)

Arguments

para

A \(G \times 4\) matrix of CP fit parameters for \(G\) features, \(G\geq 1\). The column order is intercept \(\alpha_g\), random effect \(\sigma_g^2 (\sigma_g^2\geq0)\), tweedie parameter \(p_g (1<p_g<2)\), and dispersion \(\phi (\phi>0)\).

Value

A \(G \times 1\) matrix consisting of VPC for G features based on compound Poisson mixed models. Column name is "CP-fit"; row names are the feature names.

Examples

# NOT RUN {
## Compute VPC for each feature under compound Poisson mixed models. 
vpc.cp <- computeVPC.CP(para_cp) 

## Visulize the distribution of the VPCs. 
hist(vpc.cp, breaks = 50, col = "cyan")

## Plot sorted VPCs.
plot(sort(vpc.cp), ylab = "Heritability (h2)", ylim = c(0,1), main = "Sorted CP VPC scores")
abline(h = 0.9, lty = 2, col = "red")
text(50, 0.92, "h2 = 0.9", col = "red")
# }