goodTuring(edgeR)
goodTuring()所属R语言包：edgeR
Good-Turing Frequency Estimation
图灵良好的频率估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Non-parametric empirical Bayes estimates of the frequencies of observed (and unobserved) species.
非参数的经验Bayes估计的观察和观测到的物种的频率。
用法----------Usage----------
参数----------Arguments----------
参数：x
numeric vector of non-negative integers, representing the observed frequency of each species.
非负整数，代表每个物种的观测频率，数字向量。
参数：plot
logical, whether to plot log-probability (i.e., log frequencies of frequencies)versus log-frequency.
逻辑，是否绘制log概率（即log的频率，频率）与log的频率。
参数：counts
matrix of counts
计数矩阵
Details
详情----------Details----------
Observed counts are assumed to be Poisson. Using an non-parametric empirical Bayes strategy, the algorithm evaluates the posterior expectation of each species mean given its observed count. The posterior means are then converted to proportions. In the empirical Bayes step, the counts are smoothed by assuming a log-linear relationship between frequencies and frequencies of frequencies. The basics of the algorithm are from Good (1953). Gale and Sampson (1995) proposed a simplied algorithm with a rule for switching between the observed and smoothed frequencies, and it is Gale and Sampson's simplified algorithm that is implemented here. The number of zero values in x are not used in the algorithm, but is returned by this function.
观测到的罪名被假定为泊松。该算法使用非参数的经验Bayes战略，评估后的预期意味着每个物种的观察计数。后部的手段，然后再转换为比例。在经验Bayes步骤，计数平滑假设频率之间的频率和频率的对数线性关系。该算法的基础是良好（1953年）。大风和桑普森（1995）提出了观察和平滑的频率之间切换规则1的简化算法，它是Gale和桑普森的简化算法，这里正在实施。 x不使用的算法，但由这个函数返回零值。
Sampson gives a C code version on his webpage at http://www.grsampson.net/RGoodTur.html which gives identical results to this function.
桑普森提供在http://www.grsampson.net/RGoodTur.html的一个C代码的版本，它提供了相同的结果，这个功能在他的网页。
goodTuringProportions runs goodTuring on each column of data, then uses the results to predict the proportion of each tag in each library.
goodTuringProportions运行goodTuring每列数据，然后使用结果来预测每个库中每个标签的比例。
值----------Value----------
goodTuring returns a list with components
goodTuring返回的组件列表
参数：count
observed frequencies, i.e., the unique positive values of x
观测到的频率，即，x的独特正面的价值观
参数：proportion
estimated proportion of species given the count
估计物种的比例计数
参数：P0
estimated combined proportion of all undetected species
所有未被发现的物种估计相结合的比例
参数：n0
number of zeros found in x
x零数
goodTuringProportions returns a matrix of proportions of the same size as counts.
goodTuringProportions返回一个同样大小的counts比例的矩阵。
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
Good-Turing frequency estimation without tears. Journal of Quantitative Linguistics 2, 217-237.
举例----------Examples----------
# True means of observed species[观察到的物种的真正手段]
lambda <- rnbinom(10000,mu=2,size=1/10)
lambda <- lambda[lambda>1]
# Oberved frequencies[oberved频率]
Ntrue <- length(lambda)
x <- rpois(Ntrue, lambda=lambda)
freq <- goodTuring(x, plot=TRUE)
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 2012-2-25 17:08:20
