pyunmarked

A python port of the R package unmarked, for analyzing ecological data while accounting for imperfect detection.

Fit an occupancy model

import numpy as np
import pandas as pd
from scipy.special import expit
from pyunmarked import OccupancyModel, UnmarkedFrame

# Simulate an occupancy dataset
N = 1000 # Number of sites
J = 5 # Number of sampling occasions

# Occupancy probability parameters
beta_occ = np.array([0, 0.5])
# Occupancy covariate
elev = np.random.randn(N).reshape(N,1)
# Model matrix
mm_occ = np.concatenate([np.repeat(1, N).reshape(N,1), elev], axis=1)
# Occupancy probability
psi = expit(np.matmul(mm_occ, beta_occ))

# Simulate true latent state
z = np.random.binomial(1, psi, N)

# Detection probability parameters
beta_p = np.array([0, -0.4])
# Detection covariate
wind = np.random.randn(N*J).reshape(N*J, 1)
# Model matrix
mm_det = np.concatenate([np.repeat(1,N*J).reshape(N*J,1), wind], axis=1)
# Detection probability
p = expit(np.matmul(mm_det, beta_p)).reshape(N, J)

# Simulate detection process
y = np.empty((N,J)) # presence-absence data matrix
for i in range(N):
    for j in range(J):
        y[i,j] = np.random.binomial(1, z[i]*p[i,j], 1)

# Organize covariates into DataFrames
sc = pd.DataFrame({"elev": elev.reshape(N,)})
oc = pd.DataFrame({"wind": wind.reshape(N*J,)})

# Create UnmarkedFrame containing y and covariates
umf = UnmarkedFrame(y, sc, oc)

# Fit occupancy model
mod = OccupancyModel("~wind", "~elev", umf)
mod.fit()

# Look at summary of output
mod.summary()

# Calculate predicted occupancy at each site
mod.predict("occ")