knitr::opts_chunk$set(echo = TRUE)
library(MetaLandSim)
library(plyr)
library(dplyr)
library(ggplot2)
library(pander)
library(RMark)
# Loading all population data
# Output from IBM model

df.pop <- read.csv2("c:/temp/output.txt", sep = ",")
str(df.pop)
summary(df.pop)

Properties and parameters of the population

Number of patches: r dplyr::n_distinct(df.pop$patch)

Number of timesteps: r dplyr::n_distinct(df.pop$timestep)

Number of unique individuals: r dplyr::n_distinct(df.pop$ID)

Simple CJS observation proces

Mark recapture properties CJS model

p <- 0.8  # Detection probability

EncTime <- c(20,21,22,23,24,25,26,27,28,29) # Encounter timesteps

Detection probability r p

Encounter timesteps r EncTime

df.EncHistFlat <- data.frame()

for (i in EncTime) {

  df.mark <- df.pop %>%
    dplyr::filter(timestep == i, age > 1) %>%
    dplyr::sample_frac(p, replace = FALSE) %>%
    select(timestep, ID)
  df.mark$Occ <- 1
  df.EncHistFlat <- rbind(df.EncHistFlat, df.mark)
}

str(df.EncHistFlat)

df.EncHist <- df.EncHistFlat %>%
  tidyr::spread(timestep, Occ, fill = 0) %>%
  tidyr::unite(ch, 1:length(EncTime) + 1, sep = "") %>%
  dplyr::select(ch)


df.EncHist

CJS.Phidot.pdot <- mark(data = df.EncHist)


ToonVanDaele/metapop documentation built on May 9, 2019, 5:11 p.m.