R语言 unmarked包 unmarked-package()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-10-1 13:27:04

unmarked-package(unmarked)
unmarked-package()所属R语言包：unmarked

 Models for Data from Unmarked Animals
 数据未标记的动物模型

 译者：生物统计家园网机器人LoveR

描述----------Description----------

unmarked fits hierarchical models of animal occurrence and abundance to data collected on species subject to imperfect detection. Examples include single- and multi-season site occupancy models, binomial N-mixture models, and multinomial N-mixture models. The data can arise from survey methods such as occurrence sampling, temporally replicated counts, removal sampling, double observer sampling, and distance sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. General treatment of these models can be found in MacKenzie et al. (2006) and Royle and Dorazio (2008). The primary
无人盯防的适合种不完善检测收集的数据动物发生和丰富的层次模型。例子包括单和多发季节占位模型，二项式N-混合模型，以及多项N-混合模型。从调查的方法，如事件取样，临时复制计数，清除采样，采样双观察员，距离取样的数据可能会出现。的国家和观察过程中可以模拟的协变量的函数的参数。这些模型可以在MacKenzie等人发现的一般治疗。（2006）和罗伊尔和Dorazio的（2008年）。的主要

Details

详细信息----------Details----------

Overview of Model-fitting Functions:
概述模型的拟合函数：

occu fits occurrence models with no linkage between abundance and detection (MacKenzie et al. 2002).
occu适合发生之间没有任何联系的丰度和检测（Mackenzie等人，2002）的模型。

occuRN fits abundance models to presence/absence data by exploiting the link between detection probability and abundance (Royle and Nichols 2003).
occuRN适合丰度模型存在/不存在数据，通过利用检测概率和丰度（罗伊尔和Nichols 2003）之间的链路。

colext fits the mutli-season occupancy model of MacKenzie et al. (2003).
colext适合MUTLI淡季入住率的麦肯齐等人的模型。（2003年）。

pcount fits N-mixture models (aka binomial mixture models) to repeated count data (Royle 2004a, K茅ry et al 2005).
pcount配合N-混合模型（又名的二项式混合模型），重复测量数据（罗伊尔2004年a，柯瑞等人，2005年）。

distsamp fits the distance sampling model of Royle et al. (2004) to distance data recorded in discrete intervals.
distsamp适合的距离采样模式的罗伊尔等。（2004年），远程数据记录在离散的时间间隔。

gdistsamp fits the generalized distance sampling model described by Chandler et al. (2011) to distance data recorded in discrete intervals.
gdistsamp适合Chandler等人所描述的广义距离的采样模型。（2011年）的距离数据记录在离散的时间间隔。

multinomPois fits the multinomial-Poisson model of Royle (2004b) to data collected using methods such as removal sampling or double observer sampling.
multinomPois符合多项泊松模型的罗伊尔（2004年b）收集的数据使用去除采样或双观察员的采样方法，如。

gmultmix fits a generalized form of the multinomial-mixture model of Royle (2004b) that allows for estimating availability and detection probability.
gmultmix符合多项混合模型的罗伊尔（2004年b），使可用性和检测概率估计的一个推广形式。

pcountOpen fits the open population model of Dail and Madsen (2011) to repeated count data. This is a genearlized form of the Royle (2004a) N-mixture model that includes parameters for recruitment and apparent survival.
pcountOpen适合开放式的人口模型的重复测量数据折射出马德森（2011）。这是的罗伊尔（2004年）的N-混合模型，其中包括招聘和明显的生存参数的一个genearlized形式。

Data: All data are passed to unmarked's estimation functions as a formal S4 class called an unmarkedFrame, which has child classes for each model type. This allows metadata (eg as distance interval cut points, measurement units, etc...) to be stored with the response and covariate data. See unmarkedFrame for a detailed description of unmarkedFrames and how to create them.
数据：所有的数据都传递给无人盯防的估计作为一个正式的S4的类称为一个unmarkedFrame，其中每个模型类型的子类的功能。这允许的响应和协变量数据要存储的元数据（例如，作为间隔距离的切点，测量单位，等..）。见unmarkedFrame的详细说明，的unmarkedFrames和如何建立它们的。

Model Specification: unmarked's model-fitting functions allow specification of covariates for both the state process and the detection process. For two-level hierarchical models, (eg occu, occuRN, pcount, multinomPois, distsamp) covariates for the detection process (at the site or observation level) and the state process (at the site level) are specified with a double right-hand sided formula, in that order. Such a formula looks like
型号规格：无人盯防的模型拟合函数允许指定的协变量的状态过程和检测过程中。对于两个层次的模型（如：occu，occuRN，pcount，multinomPois，distsamp）在检测过程中的协变量（在现场或观察级）和状态的过程（在现场）具有双重的右手侧式指定的顺序。这样的公式看起来像

where x1 through xn are additive covariates of the process of interest. Using two tildes in a single formula differs from standard R convention, but it is informative about the model being fit. The meaning of these covariates, or what they model, is full described in the help files for the individual functions and is not the same for all functions. For models with more than two processes (eg colext, gmultmix, pcountOpen), single right-hand sided formulas (only one tilde) are used to model each parameter.
x1通过xn添加剂协变量的过程中，利益。使用一个公式中的两个波浪号不同于标准的R公约，但它的模型是适合的信息。这些相关变量的含义，或者是从他们的模型，才得到了充分的个别功能的帮助文件中描述的所有功能是不一样的。对于模型有两个以上的进程（例如colext，gmultmix，pcountOpen），右手单双面公式（只有一个波浪号）的模型中，每个参数。

Utility Functions: unmarked contains several utility functions for organizing data into the form required by its model-fitting functions. csvToUMF converts an appropriately formated comma-separated values (.csv) file to a list containing the
实用功能：无人盯防的数据组织成所要求的形式通过其模型的拟合函数包含了几个实用程序。 csvToUMF转换为适当格式的逗号分隔值（CSV）文件到一个列表，其中包含

（作者）----------Author(s)----------

Ian Fiske, Richard Chandler, Andy Royle, and Marc K\'ery

参考文献----------References----------

density and temporary emigration in unmarked populations. Ecology 92:1429-1435.
repeated counts of an open metapopulation. Biometrics 67:577-587.
fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software 43:1–23.
replicated counts using binomial mixture models. Ecological Applications 15:1450–1461.
J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248–2255.
A. B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:2200–2207.
L. L. Bailey, and J. E. Hines. 2006. Occupancy Estimation and Modeling. Amsterdam: Academic Press.
spatially replicated counts. Biometrics 60:108–105.
count survey data. Animal Biodiversity and Conservation 27:375–386.
effects in distance sampling. Ecology 85:1591–1597.
abundance and occurrence. Journal Of Agricultural Biological And Environmental Statistics 11:249–263.
Inference in Ecology. Academic Press.
Repeated Presence-Absence Data or Point Counts. Ecology, 84:777–790.
Morrison, S.A. In Press. Hierarchical distance sampling models to estimate population size and habitat-specific abundance of an island endemic. Ecological Applications

实例----------Examples----------

## An example site-occupancy analysis[＃示例站点占用情况分析]

# Simulate occupancy data[模拟占用数据]
set.seed(344)
nSites <- 100
nReps <- 5
covariates <- data.frame(veght=rnorm(nSites),
habitat=factor(c(rep('A', 50), rep('B', 50))))

psipars <- c(-1, 1, -1)
ppars <- c(1, -1, 0)
X <- model.matrix(~veght+habitat, covariates) # design matrix[设计矩阵]
psi <- plogis(X %*% psipars)
p <- plogis(X %*% ppars)

y <- matrix(NA, nSites, nReps)
z <- rbinom(nSites, 1, psi) # true occupancy state[真正的占用状态]
for(i in 1:nSites) {
y[i,] <- rbinom(nReps, 1, z[i]*p[i])
}

# Organize data and look at it[组织数据，看看它]
umf <- unmarkedFrameOccu(y = y, siteCovs = covariates)
head(umf)
summary(umf)

# Fit some models[适合一些模型]
fm1 <- occu(~1 ~1, umf)
fm2 <- occu(~veght+habitat ~veght+habitat, umf)
fm3 <- occu(~veght ~veght+habitat, umf)

# Model selection[型号选择]
fms <- fitList(m1=fm1, m2=fm2, m3=fm3)
modSel(fms)

# Empirical Bayes estimates of the number of sites occupied[经验贝叶斯估计的网站数量占]
sum(bup(ranef(fm3), stat="mode")) # Sum of posterior modes[后模式的总和]
colSums(confint(ranef(fm3))) # 95% CI[95％CI]
sum(z) # Actual[实际]

# Model-averaged prediction and plots[模型的平均预测及图]

# psi in each habitat type[PSI在每个栖息地类型]
newdata1 <- data.frame(habitat=c('A', 'B'), veght=0)
Epsi1 <- predict(fms, type="state", newdata=newdata1)
with(Epsi1, {
plot(1:2, Predicted, xaxt="n", xlim=c(0.5, 2.5), ylim=c(0, 0.5),
 xlab="Habitat",
 ylab=expression(paste("Probability of occurrence (", psi, ")")),
 cex.lab=1.2,
 pch=16, cex=1.5)
axis(1, 1:2, c('A', 'B'))
arrows(1:2, Predicted-SE, 1:2, Predicted+SE, angle=90, code=3, length=0.05)
})

# psi and p as functions of vegetation height[植被高度PSI和P功能]
newdata2 <- data.frame(habitat=factor('A', levels=c('A','B')),
veght=seq(-2, 2, length=50))
Epsi2 <- predict(fms, type="state", newdata=newdata2, appendData=TRUE)
Ep <- predict(fms, type="det", newdata=newdata2, appendData=TRUE)

op <- par(mfrow=c(2, 1), mai=c(0.9, 0.8, 0.2, 0.2))
plot(Predicted~veght, Epsi2, type="l", lwd=2, ylim=c(0,1),
xlab="Vegetation height (standardized)",
ylab=expression(paste("Probability of occurrence (", psi, ")")))
lines(lower ~ veght, Epsi2, col=gray(0.7))
lines(upper ~ veght, Epsi2, col=gray(0.7))
plot(Predicted~veght, Ep, type="l", lwd=2, ylim=c(0,1),
xlab="Vegetation height (standardized)",
ylab=expression(paste("Detection probability (", italic(p), ")")))
lines(lower~veght, Ep, col=gray(0.7))
lines(upper~veght, Ep, col=gray(0.7))
par(op)

转载请注明:出自生物统计家园网(http://www.biostatistic.net)。

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