Kendall: Find the best Kendall's estimates of mean and environmental...

In popbio: Construction and Analysis of Matrix Population Models

Find the best Kendall's estimates of mean and environmental variance for beta-binomial vital rates

Description

Finds the best estimates of mean and environmental variance for beta-binomial vital rates, using a brute force search for the best adjusted estimates from a very large number of combinations of different possible mean and variance values.

Usage

Kendall(
  rates,
  grades = 1000,
  maxvar = 0.2,
  minvar = 1e-05,
  maxmean = 1,
  minmean = 0.01
)

Arguments

rates	a matrix or dataframe with four columns: Rate identifier, Year, Total number of starting individuals, Number growing (or surviving).
grades	number of different levels of means and variances to try, default is 1000
maxvar	maximum variance to search over, default is 0.20. The maximum possible is 0.25 and searching a narrower range will improve the accuracy of the answer.
minvar	minimum variance to search, default is 0.00001
maxmean	maximum limit on the mean values to search, default 1
minmean	minimum limit on the mean values to search, default 0.01

Details

converted Matlab code from Box 8.2 in Morris and Doak (2002)

Value

A list with estimates and confidence intervals

est	a matrix with 5 columns: (1) estimated mean, (2) Kendall's MLE mean, (3) estimated variance, (4) Kendall's MLE variance, (5) Kendall's unbiased MLE variance.
ci	a matrix with 95% confidence limits for the Kendall's mean and unbiased variance estimates with 4 columns: (1) low and (3) high mean limits, (3) low and (4) high variance limits.

Note

may deliver warning messages of: 'no finite arguments to min; returning Inf', indicating use of very low values for variance, but this is not a malfunction.

Author(s)

Adapted to R from Morris and Doak (2002: 267-270) by Patrick Nantel.

References

Kendall, B. E. 1998. Estimating the magnitude of environmental stochasticity in survivorship data. Ecological Applications 8(1): 184-193.

Morris, W. F., and D. F. Doak. 2002. Quantitative conservation biology: Theory and practice of population viability analysis. Sinauer, Sunderland, Massachusetts, USA.

See Also

varEst

Examples

## desert tortoise input from Box 8.2 - compare results to Table 8.3
tor <- data.frame(rate=rep(c("g4","g5","g6"), each=3),
   year=rep(1:3,3),      ## representing 70s, early 80s, late 80s
   start=c(17,15,7,22,19,4,32,31,10),
   grow=c(8,1,0,5,5,0,2,1,0)
)
## use fewer grades for faster loop
tor.est<-Kendall(tor, grades=200)
tor.est
wp.est <- Kendall(woodpecker, grades=200)
wp.est

popbio documentation built on May 29, 2024, 4:35 a.m.