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

Description

This function 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=0.00001, 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 ever 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

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.

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.

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.

Examples

Run this code

# NOT RUN {
## 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

data(woodpecker) 
wp.est <- Kendall(woodpecker, grades=200)
wp.est
# }