Fitting an empirical logistic spline curve for detection proportion with capped probabilities

Fitting an empirical logistic spline curve for detection proportion with capped probabilities

Usage

cappedLogit_ztbinom(
  dp,
  X,
  wt,
  alpha0 = 0.8,
  beta0,
  maxit = 100,
  trace = TRUE,
  eps = 1e-04
)

Arguments

dp

A vector of detection proportion in all precursors.

X

The basis matrix for a natural cubic spline.

wt

A vector of the numbers of trials (samples size) for zero-truncated binomial distribution.

alpha0

A start value for alpha.

beta0

Start values for the beta coefficients.

maxit

Maximum number of iterations.

trace

Logical. Whether to print out alpha estimates at each iteration.

eps

Convergence tolerance.

Value

Fitted parameters and the fitting history.

Examples

## See the vignettes.