Estimation of neutral community parameters using a two-stage maximum-likelihood procedure

Function optimal.params.sloss() returns maximum likelihood estimates of theta and m(k) using numerical optimization.

It differs from untb's optimal.params() function as it applies to a network of smaller community samples k instead of to a single large community sample.

Although there is a single, common theta for all communities, immigration estimates are provided for each local community k, sharing a same biogeographical background.

Usage

optimal.params.sloss(D, nbres = 100, ci = FALSE, cint = c(0.025, 0.975))

Arguments

D

Species counts over a network of community samples (species by sample table)

nbres

Number of resampling rounds for theta estimation

ci

Specifies whether bootstraps confidence intervals should be provided for estimates

cint

Bounds of confidence intervals, if ci = T

Value

theta

Mean theta estimate

I

The vector of estimated immigration numbers I(k)

Output of the bootstrap procedure, if ci = T:

thetaci

Confidence interval for theta

msampleci

Confidence intervals for m(k)

thetasamp

theta estimates provided by the resampling procedure

Iboot

Bootstrapped values of I(k)

mboot

Bootstrapped values of m(k)

References

Francois Munoz, Pierre Couteron, B. R. Ramesh, and Rampal S. Etienne 2007. “Estimating parameters of neutral communities: from one single large to several small samples”. Ecology 88(10):2482-2488

Author

Francois Munoz

Note

The function returns unhelpful output when run with the caruso dataset as in optimal.params.sloss(caruso). The reason for this behaviour is unknown.

See also

optimal.params, optimal.params.gst

Examples

data(ghats)
optimal.params.sloss(ghats)
#> $theta
#> [1] 53.62875
#> 
#> $m
#>  [1] 0.010177470 0.012216350 0.015685478 0.011830555 0.014240947 0.016768434
#>  [7] 0.015352210 0.012180953 0.015387777 0.015619090 0.013257703 0.010223119
#> [13] 0.006488277 0.009335648 0.016764041 0.014578575 0.012195604 0.009212344
#> [19] 0.009267170 0.012063813 0.012263035 0.011362638 0.014141649 0.005551203
#> [25] 0.012033473 0.007293550 0.022733042 0.015638402 0.012409440 0.011432190
#> [31] 0.018951753 0.017521335 0.011116481 0.006505776 0.009218522 0.012530580
#> [37] 0.020189267 0.014795946 0.019090552 0.018458212 0.015547909 0.017887769
#> [43] 0.012130589 0.013155617 0.010273853 0.014666531 0.012509133 0.015756999
#> [49] 0.012496094 0.018181228
#> 
#> $I
#>  [1]  2.2003728  3.2526354  7.1231383  2.5740214  5.5764189 10.2667549
#>  [7]  4.8489799  4.3282366  5.1885828  8.4411998  4.6219260  2.9849974
#> [13]  0.8816377  3.7600257  6.4448490  6.7165913  2.7655427  2.0827522
#> [19]  1.9362478  2.5643365  2.6320403  2.7468823  5.8095239  1.9370203
#> [25]  3.0450102  0.7567551 14.4688736  6.9266651  3.5936955  3.7584287
#> [31] 16.9031275  8.4175569  3.0689149  1.1263211  1.4793828  7.4107195
#> [37] 13.0431377  7.6142041 13.0590650 13.6714708  9.7603611 10.3817342
#> [43]  6.9133849  9.2650415  2.8131156  4.7780337  4.5476662  7.7164620
#> [49]  4.7579876  8.0552892
#>