Description Usage Arguments Details Value Note Author(s) References See Also Examples
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.
1 2 | Kendall(rates, grades = 1000, maxvar = 0.2, minvar = 1e-05,
maxmean = 1, minmean = 0.01)
|
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
converted Matlab code from Box 8.2 in Morris and Doak (2002)
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. |
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.
Adapted to R from Morris \& Doak (2002: 267-270) by Patrick Nantel.
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.
1 2 3 4 5 6 7 8 9 10 11 | ## 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
|
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