LP_modavg {Petersen}

Create an table of individual estimates and the model averaged values

Description

This will take a series of LP fits and computes the model averages for each set of N_hat

Usage

LP_modavg(..., N_hat = ~1, conf_level = 0.95)

Arguments

...

Series of LP fits

N_hat

A formula requesting which abundance estimates should be formed. The formula are expanded against the data frame to determine which records form part of the abundance estimate. The formula is evaluated against the data frame used in the fit using the model.matrix() function, and each column of the model matrix is used to form an estimate.

Some familiarity on how model.matrix() generates the model matrix of coefficients used in the expansion is needed. For example N_hat=~1 creates a model matrix with 1 column (representing the intercept) and so requests abundance over the entire population; Specifying N_hat=~-1+Sex creates a model matrix with 2 columns (one for each sex) consisting of 0/1 depending if that row of the data frame is M/F. Hence, two abundance estimates (one for each sex) is computed. On the other hand, N_hat=Sex generates a model matrix where the first column is all 1's, and a second column which is 0/1 depending if the row in the data frame is the "second" sex. Hence, this will request the overall abundance (over both sexes) and the estimate of abundance for the second sex.

In addition to the variables in the data frame, special variables include ..EF to allow access to the expansion factor so you can request a "truncated" Horvitz-Thompson estimator using N_hat=~-1+I(as.numeric(..EF<1000)) to only use those animals with expansion factors less than 1000 in forming the estimate.

conf_level

The expected coverage for confidence intervals on N.

Value

An data frame with model averaged values for abundance

Examples

data(data_rodli)
mt <- Petersen::LP_fit(data=data_rodli, p_model=~..time)
m0 <- Petersen::LP_fit(data=data_rodli, p_model=~1)
Petersen::LP_modavg(m0,mt)