F.efficiency.model: F.efficiency.model

In tmcd82070/CAMP_RST: CAMP RST R Routines

F.efficiency.model

Description

Estimate the efficiency model from a data frame containing efficiency trials.

Usage

F.efficiency.model(obs.eff.df, plot = T, max.df.spline = 4, plot.file = NA)

Arguments

obs.eff.df	A data frame with at least variables batchDate and efficiency, where efficiency is NA for all days requiring an estimate.
plot	A logical indicating if raw efficiencies and the model(s) should be plotted.
max.df.spline	The maximum degrees of freedom allowed for splines.
plot.file	The name of the file prefix under which output is to be saved. Set to NA to plot to the plot window.

Value

A data frame with all observed and imputed efficiency values, where variable gam.estimated identifies days with imputed values.

Efficiency model method

• Less than eff.min.spline.samp.size trials : A "weighted average constant model with bias correction." This model uses constant efficiency over the season, and estimates it using a ratio-of-means bias-corrected ("ROM+1") average. For each trap, estimated efficiency is

  \frac{(∑ nCaught) + 1}{(∑ nReleased) + 1}{(sum(nCaught) + 1) / (sum(nReleased) + 1)}

  . Values for nCaught and nReleased come from function F.get.releases.

• eff.min.spline.samp.size trials or more : A "B-spline model." This model starts by estimating a constant logistic regression where recaptures (i.e., nCaught) is the number of "successes" and releases (i.e., nReleased) is number of "trials". Assuming this constant model is successful, the method estimates a series of increasingly complex b-spline logistic regression models until AIC is minimized or model estimation fails (failure to converge or estimates at boundary). B-spline models, in general, divide the date range into intervals by adding 'knots'. Between 'knots', b-spline models fit cubic polynomials in a way that connects smoothly at knots (refer to b-spline methods for details).

  The first (lowest order) b-spline model fitted contains zero knots and therefore estimates a cubic model. Assuming that model was successful and that AIC improved relative to the constant model, the method adds one knot at the median date and re-estimates. If that model was successful and AIC improved relative to the previous model, the method adds another knot at the (1/(knots+1))-th quantiles of date and re-estimates. The method containues to add knots until one or more of the following conditions happen: (1) AIC does not improve, (2) estimation fails somehow, or (3) the maximum number of knots (i.e., max.df.spline-3) is fitted.

  Using the default value of max.df.spline, the efficiency model is either constant (intercept-only), cubic, or b-spline with one interval knot.

  When the best logistic regression model is constant (intercept-only), estimated efficiency is the ratio-of-means estimator WITHOUT the "+1" bias correction. With many efficiency trial, the "+1" bias correction is tiny and inconsequential. The exact efficiency model used at each subsite is listed in the campR log file.

  The βs from the final logistic regression are saved for use in bootstrapping by function F.boostrap.passage. Modeled efficiencies are used for all days, even if a particular day contained an efficiency trial.

  All dates outside the efficiency trial season use the mean of estimates within the season. This means the efficiency model can vary within a season, but is always constant before the first and after the last efficiency trial.

Author(s)

WEST Inc.

See Also

F.get.releases, F.bootstrap.passage, reMap, reMap2

Examples

## Not run: 
#   ---- Fit an efficiency model for each unique trapPositionID 
#   ---- in data frame obs.eff.df.  
F.efficiency.model( obs.eff.df, plot=T, max.df.spline=4, plot.file=NA)

## End(Not run)

tmcd82070/CAMP_RST documentation built on April 6, 2022, 12:07 a.m.