안녕하세요 . phylogentic 에서 사용하는 "bootstrap value" 로써 표본오차를 구하는 방법입니다. 저도 정확히, 자세히는 몰라서 설명이 잘되어있는 인터넷 출처에서 따온 내용을 올리겠습니다.
In the statistical context, bootstrapping refers to using the data at hand to infer the uncertainty of said data. I.e. improve the statistic by pulling on its bootstraps. In practice, this is achieved by sampling or permuting the input data.
As an example, consider a sample of 100 sheep of which you have tested their buoyancy. You can give the mean and standard deviation of these 100 sheep, but we wish to infer more about the general buoyancy of sheep. However, after our experiments, we are banned from the farm and cannot test more sheep. When taking a sample of a population, we assume they are representative of the entire population; thus if we repetitive sample by replacement from our 100 sheep and calculate the same test statistics, we get an idea of the whole population. This is the basic idea of bootstrapping.
In terms of your phylogenetic tree, the bootstrapping values indicates how many times out of 100 (in your case) the same branch was observed when repeating the phylogenetic reconstruction on a re-sampled set of your data. If you get 100 out of 100 (and your data is sufficiently large to support this), we are pretty damned sure that the observed branch is not due to a single extreme datapoint. If you get 50 out of 100, we cannot be as certain.
부트스트랩
데이터에서 얻어진 통계량의 표본오차를 확률 분포의 가정을 두지 않고 논 파라메트릭(non-para-metric)하게 평가하기 위한 하나의 방법. 이 방법에서는 주어진 데이터세트를 원래의 모집단을 대표하는 독립 표본으로 가정하고, 그 자료로부터 중복을 허용한 무작위 재추출로 복수의 자료를 작성하고 각각에서 얻어진 통계량을 계산함. 계통추정론의 분야에서 계통수의 신뢰성을 평가할 목적으로 널리 사용되고 있음.
