unpmle(SPECIES)
unpmle()所属R语言包：SPECIES
Unconditional NPML estimator for the SPECIES number
无条件NPML估计的物种数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculate the unconditional NPML estimator of the species number by Norris and Pollock 1996, 1998. This estimator was obtained from the full likelihood based on a Poisson mixture model. The confidence interval is calculated based on a bootstrap procedure.
这个函数计算的无条件NPML诺里斯和波洛克1996年，1998年的物种数估计。这估计是从充分的可能性，基于上一个泊松混合物模型获得。置信区间的计算自举程序。
用法----------Usage----------
unpmle(n,t=15,C=0,method="W-L",b=200,conf=.95,seed=NULL,dis=1)
参数----------Arguments----------
参数：n
a matrix or a numerical data frame of two columns. It is also called the “frequency of frequencies” data in literature. The first column is the frequency j=1, 2…; and the second column is n_j, the number of species observed with j individuals in the sample.
两列的矩阵或数值数据框。它也被称为“频率”在文献中的数据的频率。第一列是频率j=1, 2…;，第二列是n_j，j样品中的个人观察到的物种的数量。
参数：t
a positive integer. t specifies the cutoff value to define the relatively less abundant species to be used in estimation. The default value for t=15. The estimator is fairly insensitive to the choice of t. The recommendation is to use t ≥ 10.
一个正整数。 t指定的截止值，以定义要用于估计的相对较少的丰富的物种。默认值T = 15。该估计是相当不敏感的选择t。的建议使用t ≥ 10。
参数：C
integer either 0 or 1. It specifies whether bootstrap confidence interval should be calculated. “C=1” for YES and “C=0” for NO.The default of C is set as 0.
整数0或1。它指定是否应计算自举置信区间。 “C= 1”为YES“C= 0”默认C NO.The被设置为0。
参数：method
string either “N-P” or “W-L”(default). If method=“N-P”, unconditional NPMLE will be used using an algorithm by Bonhing and Schon (2005). Sometimes this method can be extremely slow. Alternatively one can use method “W-L”, an approximate method (but with high precision and much faster) by Wang and Lindsay 2005.
字符串为“N-P”或“W-L”（默认值）。如果method=“N-P”，无条件NPMLE;将被用于使用一个算法Bonhing和舍恩（2005）。这种方法有时可能会非常慢。另一种方法是，可以使用方法“W-L”，一种近似方法（但具有高的精确度和更快）由王和Lindsay 2005。
参数：b
integer. b specifies the number of bootstrap samples for confidence interval. It is ignored if “C=0”.
整数。 b指定的bootstrap样本的置信区间。它被忽略，如果“C= 0”。
参数：conf
a positive number ≤ 1. conf specifies the confidence level for confidence interval. The default is 0.95.
一个正数≤ 1。 conf指定的置信水平下的置信区间。默认值是0.95。
参数：seed
a single value, interpreted as an integer. Seed for random number generation
一个单一的值，解释为一个整数。产生随机数的种子
参数：dis
0 or 1. 1 for on-screen display of the mixture output, and 0 for none.
0或1。 1的混合物输出的屏幕上的显示，并没有为0。
Details
详细信息----------Details----------
The computing is intensive if method=“N-P” is used particularly when extrapolation is large. It may takes hours to compute the bootstrap confidence interval. If method=“W-L” is used, computing usually
如果method=“N-P”，特别是当采用外推法是大的计算是密集的。它可能需要几个小时来计算引导的置信区间。如果method=“W-L”，计算通常
值----------Value----------
The function unpmle returns a list of: Nhat, CI (if “C=1”) <table summary="R valueblock"> <tr valign="top"><td>Nhat</td> <td> point estimate of N</td></tr> <tr valign="top"><td>CI</td> <td> bootstrap confidence interval.</td></tr> </table>
函数unpmle返回一个列表：Nhat，CI（如果“C= 1”）<table summary="R valueblock"> <TR VALIGN =“顶“<TD> Nhat </ TD> <TD>点估计N </ TD> </ TR> <tr valign="top"> <TD> CI </ TD> <TD>引导的置信区间。</ TD> </ TR> </ TABLE>
注意----------Note----------
The unconditional NPML estimator is unstable from either method='N-P' or method='W-L'. Extremely large estimates may occur.
无条件NPML估计是method='N-P'或method='W-L'是不稳定的。非常大的估计可能发生。
（作者）----------Author(s)----------
Ji-Ping Wang, Department of Statistics, Northwestern University
参考文献----------References----------
Norris, J. L. I., and Pollock, K. H. (1996), Nonparametric MLE Under Two Closed Capture-Recapture Models With Heterogeneity, Biometrics, 52,639-649.
Norris, J. L. I., and Pollock, K. H.(1998), Non-Parametric MLE for Poisson Species Abundance Models Allowing for Heterogeneity Between Species, Environmental and Ecological Statistics, 5, 391-402.
Bonhing, D. and Schon, D., (2005), Nonparametric maximum likelihood estimation of population size based on the counting distribution, Journal of the Royal Statistical Society, Series C: Applied Statistics, 54, 721-737.
Wang, J.-P. Z. and Lindsay, B. G. ,(2005), A penalized nonparametric maximum likelihood approach to species richness estimation. Journal of American Statistical Association, 2005,100(471):942-959
实例----------Examples----------
library(SPECIES)
##load data from the package, [＃加载数据从包中]
## "butterfly" is the famous butterfly data by Fisher 1943.[“蝴蝶”是著名的蝴蝶费舍尔1943年的数据。]
data(butterfly)
##output estimate without confidence interval using cutoff t=15[＃输出的置信区间估计没有使用截止吨= 15]
#unpmle(butterfly,t=15,C=0)[unpmle（蝶泳，T = 15，C = 0）]
##output estimate with confidence interval using cutoff t=15[＃输出估计值的置信区间使用截止吨= 15]
#unpmle(butterfly,t=15,C=1,b=200)[unpmle，T = 15，C = 1，B = 200（蝴蝶）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 14:49:25
