subtrialCR | R Documentation |
These functions create one subsampling trial dataset with a desired subsampling method
subtrialCR( x, q, bin = NULL, unit = NULL, keep = NULL, useFailed = FALSE, showFailed = FALSE ) subtrialOXW( x, q, bin = NULL, coll = NULL, xexp = 1, keep = NULL, useFailed = FALSE, showFailed = FALSE ) subtrialSQS( x, tax, q, bin = NULL, coll = NULL, ref = NULL, singleton = "occ", excludeDominant = FALSE, largestColl = FALSE, fcorr = "good", byList = FALSE, keep = NULL, useFailed = FALSE, showFailed = FALSE, appr = "under" )
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The essence of these functions are present within the subsampling wrapper function subsample
. Each function implements a certain subsampling type.
The return value of the funcfions by default is a logical
vector indicating which rows of the original dataset should be present in the subsample.
The inexact method for SQS is implemented here as it is computationally less demanding.
References:
Alroy, J., Marshall, C. R., Bambach, R. K., Bezusko, K., Foote, M., Fürsich, F. T., … Webber, A. (2001). Effects of sampling standardization on estimates of Phanerozoic marine diversification. Proceedings of the National Academy of Science, 98(11), 6261-6266.
Alroy, J. (2010). The Shifting Balance of Diversity Among Major Marine Animal Groups. Science, 329, 1191-1194. https://doi.org/10.1126/science.1189910
Raup, D. M. (1975). Taxonomic Diversity Estimation Using Rarefaction. Paleobiology, 1, 333-342. https: //doi.org/10.2307/2400135
A logical vector.
#one classical rarefaction trial data(corals) # return 5 references for each stage bRows<-subtrialCR(corals, bin="stg", unit="reference_no", q=5) # control unCor<-unique(corals[bRows,c("stg", "reference_no")]) table(unCor$stg)
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