ranef-methods(unmarked)
ranef-methods()所属R语言包：unmarked
Methods for Function ranef in Package unmarked
在包装无人盯防的功能ranef方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate posterior distributions of the random variables (latent abundance or occurrence) using empirical Bayes methods. These methods return an object storing the posterior distributions of the latent variables at each site, and for each year (primary period) in the case of open population models. See unmarkedRanef-class for methods used to manipulate the returned object.
估计后验分布的随机变量（潜在的数量或发生），使用经验Bayes方法。这些方法返回一个对象存储在每个站点，并每年（初级阶段）的情况下，开放的人口模型的潜变量的后验分布。请参阅unmarkedRanef类，用于操作返回的对象的方法。
方法----------Methods----------
signature(object = "unmarkedFitOccu") Computes the conditional distribution of occurrence given the data and the estimates of the
signature(object = "unmarkedFitOccu")计算的条件分布的发生给定的数据和估计
signature(object = "unmarkedFitOccuRN") Computes the conditional abundance distribution given the data and the estimates of the fixed effects, <i>Pr(N(i)=k |
signature(object = "unmarkedFitOccuRN")计算条件的丰度分布的数据和估计的固定效应，<I> PR（N（I）= K |
警告----------Warning----------
Empirical Bayes methods can underestimate the variance of the posterior distribution because they do not account for uncertainty in the hyperparameters (lambda or psi). However, we have not found this to be the case in a set of limited simulations studies that can be found in the inst/unitTests directory of unmarked. Simulations do, however, indicate that the posterior mode can exhibit slight (3-5 percent) negative bias as a point estimator of site-specific abundance. It appears to be safer to use the posterior mean even though this will not be an integer in general.
经验贝叶斯方法不能低估的后验分布的方差，因为他们没有考虑到的超（λ或psi）的不确定性。然而，我们还没有发现这是一组有限的模拟研究，可以发现在研究所/单元测试目录无人盯防的情况下。不必说什么，否则情况更糟，不过，模拟后的模式可以表现出轻微的负偏压（3-5％）作为一个点估计特定地点的丰富。这似乎是更安全的使用后验均值即使这不会是一般的整数。
注意----------Note----------
From Carlin and Louis (1996): “... the Bayesian approach to inference depends on a prior distribution for the model parameters. This prior can depend on unknown parameters which in turn may follow some second-stage prior. This sequence of parameters and priors consitutes a hierarchical model. The hierarchy must stop at some point, with all remaining prior parameters assumed known. Rather than make this assumption, the basic empirical Bayes approach uses the observed data to estimate these final stage parameters (or to estimate the Bayes rule), and proceeds as in a standard Bayesian analysis.”
从卡林和路易斯（1996年）：“...贝叶斯方法的推理依赖于模型参数的先验分布为。这之前可以依赖于未知参数，从而可能遵循一些第二阶段前。这种序列的参数和先验consitutes的分层模型。层次结构必须停止在某些时候，所有剩余的先验参数假设已知的。而不是让这个假设，基本经验贝叶斯方法使用的观测数据估计这些最后阶段的参数（或估计的贝叶斯规则），并继续在一个标准的贝叶斯分析“。
（作者）----------Author(s)----------
Richard Chandler <a href="mailto:rchandler@usgs.gov">rchandler@usgs.gov</a>
参考文献----------References----------
based on bootstrap samples. Journal of the American Statistical Association 82:739–750.
Data Analysis. Chapman and Hall/CRC.
in Ecology. Academic Press.
参见----------See Also----------
unmarkedRanef-class
unmarkedRanef级
实例----------Examples----------
# Simulate data under N-mixture model[N-混合模型下的模拟数据]
set.seed(4564)
R <- 20
J <- 5
N <- rpois(R, 10)
y <- matrix(NA, R, J)
y[] <- rbinom(R*J, N, 0.5)
# Fit model[拟合模型]
umf <- unmarkedFramePCount(y=y)
fm <- pcount(~1 ~1, umf, K=50)
# Estimates of conditional abundance distribution at each site[在每个站点有条件的丰度分布估计]
(re <- ranef(fm))
# Best Unbiased Predictors[最佳无偏的预测]
bup(re, stat="mean") # Posterior mean[验均值]
bup(re, stat="mode") # Posterior mode[后路模式]
confint(re, level=0.9) # 90% CI[90％CI]
# Plots[图]
plot(re, subset=site %in% c(1:10), layout=c(5, 2), xlim=c(-1,20))
# Compare estimates to truth[比较真理的估计“]
sum(N)
sum(bup(re))
colSums(confint(re))
# Extract all values in convenient formats[可以很方便地提取物中的所有值]
post.df <- as(re, "data.frame")
head(post.df)
post.arr <- as(re, "array")
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 2012-10-1 13:26:25
