Fitting an empirical logistic spline curve for detection proportion

Fitting an empirical logistic spline curve for detection proportion

Usage

logit_ztbinom(dp, X, wt, beta0, b0.upper = 0, b1.upper = Inf)

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.

beta0

Start values for the beta coefficients.

b0.upper

Upper bound for b0.

b1.upper

Upper bound for b1.

Value

Fitted beta coefficients and the fitting history.

Examples

## See the vignettes.