fitNmixPara {quickNmix}

Fit Asymptotic N-mixture Model Using optimParallel

Description

Fit an open population N-mixture model using the asymptotic approximation. The four parameters are mean initial site abundance lambda, mean recruitments gamma, survival probability omega, and probability of detection pdet. Parameters can be made to vary over sites and over times by including parameter covariates. Note that this function is essentially a wrapper for optim acting on the nll function.

Usage

fitNmixPara(
  cluster,
  nit,
  K = NULL,
  starts = NULL,
  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,
  LowerBounds = NULL,
  ...
)

Arguments

cluster

cluster object created using makeCluster, for example: cl <- makeCluster(parallel::detectCores()-1)

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). If K=NULL, K=5*max(nit) will be used as default. Default: NULL

starts

Either NULL for default starting values, or a vector of parameter values: c(log(lambda), log(gamma), logit(omega), logit(pdet)). Note that the parameter vector will need to be longer by one for each parameter coefficient if covariate values are supplied. The order of coefficients is: c(lambda, l_s_c, gamma, g_s_c, g_t_c, omega, o_s_c, o_t_c, pdet, p_s_c, p_t_c)

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 during model fitting, Default: FALSE

outfile

Location of csv file to write/append parameter values, can be used to checkpoint long running model fits. Default: NULL

LowerBounds

Lower bounds to be passed to optimParallel (if NULL, default values will be used), you may need to set this manually if you receive errors such as: "L-BFGS-B needs finite values of 'fn'".

...

Additional arguments passed to the optimization function optimParallel.

Value

Returns the fitted model object.

Examples

if (interactive()) {
cl <- makeCluster(parallel::detectCores()-1) # number of clusters should be 2*p+1 for optimal gains
nit = matrix(c(1,1,0,1,1,2,2), nrow=1) # observations for 1 site, 7 sampling occassions
model1 = fitNmixPara(cl, nit, K=100) # fit the model with population upper bound K=100
parallel::stopCluster(cl)
}