analysisLambda: Reconstruct Posterior Annual Growth Rate in Different...

In YunyiShen/ReDDLeslie: Reconstruct Culling and Aerial count of Population

Reconstruct Posterior Annual Growth Rate in Different Scenario

Usage

analysisLambda(mcmc_obj, Assumptions = list(), nage, n_proj)

Arguments

mcmc_obj	A list, mcmc.obj from ReCAP_sampler result
Assumptions	Assumption matrices used in ReCAP_sampler
nage	Number of age classes, female first, same as ReCAP_sampler
n_proj	Number of years did projection, usually ncol(Harvest_data)-1

Value

A list of class "ReCAP_lambda" has same length as number of MCMC samples taken in ReCAP_sampler. Each element is one posterior sample which is a matrix, with each column as years. Rows are anaual growth rate of: maximum possible, uniform age structure, stable age structure, observed age structure w/o harvest, minimum possible, observed growth rate (w/ harvest).

Author(s)

Yunyi Shen

Examples

set.seed(42)
ReCAP_sample = ReCAP_sampler(ChicagoDeerdata$Harv.data
    ,ChicagoDeerdata$Aeri.data
    ,ChicagoDeerdata$nage
    ,ChicagoDeerdata$mean.f
    ,ChicagoDeerdata$mean.s
    ,ChicagoDeerdata$mean.SRB
    ,ChicagoDeerdata$mean.H
    ,ChicagoDeerdata$mean.A
    ,n.iter = 50,burn.in = 5,thin.by = 1
    ,Assumptions = ChicagoDeerdata$Assumptions)

analysisoflambda = analysisLambda(ReCAP_sample$mcmc.objs
    ,ChicagoDeerdata$Assumptions
    ,ChicagoDeerdata$nage
    ,ChicagoDeerdata$proj.periods)

Lambda_plot(analysisoflambda,1992,0.05)

YunyiShen/ReDDLeslie documentation built on April 1, 2022, 6:58 a.m.