efp {ef}

Estimate capture probabilites from electrofishing data

Description

This function uses the marginal likelihood of capture probabilities to estimate model parameters

Usage

efp(
  formula,
  data = NULL,
  pass = pass,
  id = id,
  offset = NULL,
  verbose = FALSE,
  init = "0",
  hessian = TRUE,
  fit = TRUE,
  sample_re = FALSE
)

Arguments

formula

a formula object

data

a data.frame containing all relavent info

pass

a vector of integers giving the pass number of the observation

id

a vector of integers identifying an observation (a set of electrofishing passes)

offset

an possible offset for the linear predictor of capture probability

verbose

if TRUE stan optimiser messages are printed to the screen

init

should initialisatiom be random?

hessian

if TRUE the hessian is computed and the covariance matrix of the parameters is returned via Vb

fit

if TRUE model is fitted if FALSE the data that would be passed to the optimiser is returned

sample_re

should sample random effects be included

Value

glm type object

Examples

# create two electrofishing site visits with 3 and 4 passes and 2 lifestages
ef_data <- data.frame(n      = c(100, 53, 24, 50, 26, 12,
                                 100, 53, 24, 50, 26, 12),
                      pass   = c(  1,  2,  3,  1,  2,  3,
                                   1,  2,  3,  1,  2,  3),
                      stage  = c(  1,  1,  1,  2,  2,  2,
                                   1,  1,  1,  2,  2,  2),
                      sample = c(  1,  1,  1,  2,  2,  2,
                                   3,  3,  3,  4,  4,  4))

ef_data2 <- data.frame(n      = c(100, 53, 24, 50, 26, 12,
                                  100, 53, 24, 12, 50, 26, 12, 6),
                       pass   = c(  1,  2,  3,  1,  2,  3,
                                    1,  2,  3,  4,  1,  2,  3, 4),
                       stage  = c(  1,  1,  1,  2,  2,  2,
                                    1,  1,  1,  1,  2,  2,  2, 2),
                       sample = c(  1,  1,  1,  2,  2,  2,
                                    3,  3,  3,  3,  4,  4,  4, 4))

ef_data3 <- data.frame(n      = c(100, 53, 24, 50, 26, 12, 40,
                                  100, 53, 24, 12, 50, 26, 12, 6, 40),
                       pass   = c(  1,  2,  3,  1,  2,  3, 1,
                                    1,  2,  3,  4,  1,  2,  3, 4, 1),
                       stage  = c(  1,  1,  1,  2,  2,  2, 1,
                                    1,  1,  1,  1,  2,  2,  2, 2, 2),
                       sample = c(  1,  1,  1,  2,  2,  2, 5,
                                    3,  3,  3,  3,  4,  4,  4, 4, 6))

# Fit a simple model
m2 <- efp(n ~ 1 + factor(stage), data = ef_data, pass = pass, id = sample)
cbind(ef_data, fit = fitted(m2))
m3 <- efp(n ~ 1 + factor(stage), data = ef_data2, pass = pass, id = sample)
cbind(ef_data2, fit = fitted(m3))
m4 <- efp(n ~ 1 + factor(stage), data = ef_data3, pass = pass, id = sample)
cbind(ef_data3, fit = fitted(m4))

# create two electrofishing site visits with 3 and 4 passes and 2 lifestages
ef_data <- data.frame(n      = c(200, 53, 24, 100, 26, 12,
                                 200, 53, 24, 100, 26, 12),
                      pass   = c(  1,  2,  3,  1,  2,  3,
                                   1,  2,  3,  1,  2,  3),
                      stage  = c(  1,  1,  1,  2,  2,  2,
                                   1,  1,  1,  2,  2,  2),
                      sample = c(  1,  1,  1,  2,  2,  2,
                                   3,  3,  3,  4,  4,  4))
# Fit a simple model
m2 <- efp(n ~ 1 + factor(stage) + factor(replace(pass, pass> 2, 2)),
          data = ef_data, pass = pass, id = sample)
out <- cbind(ef_data, p = fitted(m2, type = "p"))
out