popbio (version 2.7)

Kendall: 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)

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.

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

Author

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

Details

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

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