varEst

a matrix or dataframe with four columns: Rate identifier, Year,
Total number of starting individuals, Number surviving (or growing)

rates

either 1 for weighted average demographic variance, or 0 for
unweighted average, default is 1

weighted

Finds the best estimates of mean and environmental variance for beta-binomial
vital rates using the approximation method of Akcakaya (2002)

Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).

Chris Stubben

popbio

Construction and Analysis of Matrix Population Models

varEst function

<dl><dt>rates</dt>
<dd>a matrix or dataframe with four columns: Rate identifier, Year,
Total number of starting individuals, Number surviving (or growing)</dd>
<dt>weighted</dt>
<dd>either 1 for weighted average demographic variance, or 0 for
unweighted average, default is 1</dd></dl>

Arguments

Patrick Nantel, 20 June 2005. Last modified May 1st 2007.

Author

Estimate the variance of beta-binomial vital rates — varEst

<dl>

<dt>rates</dt>
<dd>a matrix or dataframe with four columns: Rate identifier, Year,
Total number of starting individuals, Number surviving (or growing)</dd>


<dt>weighted</dt>
<dd>either 1 for weighted average demographic variance, or 0 for
unweighted average, default is 1</dd>

</dl>

varEst: Estimate the variance of beta-binomial vital rates

Description

Usage

Value

Arguments

Author

References

See Also

Examples