In ToonVanDaele/metapop:

knitr::opts_chunk$set(echo = TRUE)
library(MetaLandSim)
library(plyr)
library(dplyr)
library(ggplot2)
library(pander)
library(MetaLandSim)

# Loading all population data
# Output from IBM model

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

nTimeStep <- dplyr::n_distinct(df.pop$timestep)

Properties and parameters of the population

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

Number of timesteps: r nTimeStep

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

Simple presence/absence observation process

Assumptions:

• ...

p <- 0.5  # Detection probability

v.Obs <- seq(20, 50, 5)  # Observation time steps

Parameters:

• detection probability r p

• Presence / absencce detection `

df.ObsFlat <- data.frame()

for (i in v.Obs) {

  df.temp <- df.pop %>%
    dplyr::filter(timestep == i, age > 1) %>%
    dplyr::sample_frac(p, replace = FALSE) %>%
    dplyr::select(patch, timestep) %>%
    dplyr::count(patch) %>%
    cbind(i)

  df.ObsFlat <- rbind(df.ObsFlat, df.temp)
}

str(df.ObsFlat)

df.Obs <- df.ObsFlat %>%
  dplyr::mutate(PA = min(n)) %>%
  dplyr::select(patch, i, PA) %>%
  tidyr::spread(i, PA, fill = 0)

 data(occ.landscape) 

 str(occ.landscape)
parameter.estimate(sp = occ.landscape,
                   method = "Rsnap_1",
                   alpha = FALSE,
                   nsnap = 1)

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