nll: Negative Log Likelihood

In quickNmix: Asymptotic N-Mixture Model Fitting

Negative Log Likelihood

Description

Computes the negative log likelihood function for the open population asymptotic N-mixtures model.

Usage

nll(
  par,
  nit,
  K,
  l_s_c = NULL,
  g_s_c = NULL,
  g_t_c = NULL,
  o_s_c = NULL,
  o_t_c = NULL,
  p_s_c = NULL,
  p_t_c = NULL,
  SMALL_a_CORRECTION = FALSE,
  VERBOSE = FALSE,
  outfile = NULL
)

Arguments

par	Vector of parameter values: c(log(lambda), log(gamma), logit(omega), logit(pdet)). Note that the parameter vector will need to be longer if covariate values are supplied.
nit	Matrix of counts data. Rows represent sites, columns represent sampling occasions. Note that if the data is a vector, then it will be converted to a matrix with a single row.
K	Upper bound on summations in the likelihood function. K should be chosen large enough that the negative log likelihood function is stable (unchanging as K increases).
l_s_c	List of lambda site covariates, Default: NULL
g_s_c	List of gamma site covariates, Default: NULL
g_t_c	List of gamma time covariates, Default: NULL
o_s_c	List of omega site covariates, Default: NULL
o_t_c	List of omega time covariates, Default: NULL
p_s_c	List of pdet site covariates, Default: NULL
p_t_c	List of pdet time covariates, Default: NULL
SMALL_a_CORRECTION	If TRUE will apply the small a correction when calculating the transition probability matrix, Default: FALSE
VERBOSE	If TRUE, will print additional information, Default: FALSE
outfile	Location of csv file to write/append parameter values, Default: NULL

Details

DETAILS

Value

Returns the negative log likelihood function evaluated at par.

Examples

if (interactive()) {
nit = matrix(c(1,1,0,1,1), nrow=1) # observations for 1 site, 5 sampling occassions
par = c(1,1,1,0) # parameter values at which to calculate the negative log likelihood (nll)
nll(par, nit, K=10) # nll with K=10
nll(par, nit, K=10, SMALL_a_CORRECTION=TRUE) # nll with small a correction
}

quickNmix documentation built on March 21, 2022, 9:11 a.m.