gdistsamp(unmarked)
gdistsamp()所属R语言包：unmarked
Fit the generalized distance sampling model of Chandler et al. (2011).
符合广义距离采样钱德勒等人的模型。 （2011年）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Extends the distance sampling model of Royle et al. (2004) to estimate the probability of being available for detection. Also allows abundance to be modeled using the negative binomial distribution.
扩展的距离抽样模型的罗伊尔等。 （2004），来估计可用于检测的概率。也可以让丰富使用负二项分布进行建模。
用法----------Usage----------
gdistsamp(lambdaformula, phiformula, pformula, data, keyfun =
c("halfnorm", "exp", "hazard", "uniform"), output = c("abund",
"density"), unitsOut = c("ha", "kmsq"), mixture = c("P", "NB"), K,
starts, method = "BFGS", se = TRUE, rel.tol=1e-4, ...)
参数----------Arguments----------
参数：lambdaformula
A right-hand side formula describing the abundance covariates.
右手侧配方，丰富的协变量的描述。
参数：phiformula
A right-hand side formula describing the availability covariates.
右手侧式描述的可用性协变量。
参数：pformula
A right-hand side formula describing the detection function covariates.
右手边公式描述的检测功能的协变量。
参数：data
An object of class unmarkedFrameGDS
对象的类unmarkedFrameGDS
参数：keyfun
One of the following detection functions: "halfnorm", "hazard", "exp", or "uniform." See details.
以下检测功能：“halfnorm”，“危险”，“EXP”，或“统一”。查看详细信息。
参数：output
Model either "density" or "abund"
型号要么“密度”或“abund”
参数：unitsOut
Units of density. Either "ha" or "kmsq" for hectares and square kilometers, respectively.
密度单位。无论是“哈”或“kmsq公顷，平方公里，分别。
参数：mixture
Either "P" or "NB" for the Poisson and negative binomial models of abundance.
无论是“P”或“NB”的泊松分布和负二项分布模型的丰富。
参数：K
An integer value specifying the upper bound used in the integration.
一个整数值，指定上结合于一体。
参数：starts
A numeric vector of starting values for the model parameters.
甲数值向量开始的模型参数的值。
参数：method
Optimization method used by optim.
优化所使用的方法optim。
参数：se
logical specifying whether or not to compute standard errors.
逻辑指定是否计算标准误差。
参数：rel.tol
relative accuracy for the integration of the detection function. See integrate. You might try adjusting this if you get an error message related to the integral. Alternatively, try providing different starting values.
检测功能为一体的相对准确度。请参阅整合。你可以尝试调整，如果你得到一个错误信息有关的积分。或者，尝试提供不同的初始值。
参数：...
Additional arguments to optim, such as lower and upper bounds
其他参数OPTIM，如上限和下限
Details
详细信息----------Details----------
This model extends the model of Royle et al. (2004) by estimating the probability of being available for detection phi. This effectively relaxes the assumption that g(0)=1. In other words, inividuals at a distance of 0 are not assumed to be detected with certainty. To estimate this additional parameter, replicate distance sampling data must be collected at each transect. Thus the data are collected at i = 1, 2, ..., R transects on t = 1, 2, ..., T occassions. As with the model of Royle et al. (2004), the detections must be binned into distance classes. These data must be formatted in a matrix with R rows, and JT columns where J is the number of distance classses. See unmarkedFrameGDS for more information.
该模型扩展了模型的罗伊尔等。 （2004年），通过估计的概率是可用于检测phi。这有效地松弛的假设，g(0)=1。换句话说，在距离为0的inividuals被假定为不能肯定地检测。估计这个额外的参数，复制距离采样数据必须被收集在每个样。因此，数据被收集在i = 1，2，...，R断面在t = 1，2，...，T occassions。至于模型罗伊尔等。 （2004年），必须分级侦测到距离班。这些数据必须被格式化为与R行和JT列，其中J是距离classses数的矩阵。见unmarkedFrameGDS更多信息。
值----------Value----------
An object of class unmarkedFitGDS.
对象的类unmarkedFitGDS。
注意----------Note----------
If you aren't interested in estimating phi, but you want to use the negative binomial distribution, simply set numPrimary=1 when formatting the data.
如果你不感兴趣估计Phi，但您要使用的负二项分布，只需设置numPrimary = 1时的数据格式。
注意----------Note----------
You cannot use obsCovs, but you can use yearlySiteCovs (a confusing name since this model isn't for multi-year data. It's just a hold-over from the colext methods of formatting data upon which it is based.)
您不能使用obsCovs，但您可以使用yearlySiteCovs（一个令人困惑的名字，因为这种模式不适合多年的数据，它只是一个保持的colext的格式化数据的方法，它是基于。）
（作者）----------Author(s)----------
Richard Chandler <a href="mailto:rchandler@usgs.gov">rchandler@usgs.gov</a>
参考文献----------References----------
abundance effects in distance sampling. Ecology 85:1591-1597.
density and temporary emigration in unmarked populations. Ecology 92:1429–1435.
参见----------See Also----------
distsamp
distsamp
实例----------Examples----------
# Simulate some line-transect data[模拟一些线路断面数据]
set.seed(36837)
R <- 50 # number of transects[样条数]
T <- 5 # number of replicates[重复次数]
strip.width <- 50
transect.length <- 100
breaks <- seq(0, 50, by=10)
lambda <- 5 # Abundance[丰富]
phi <- 0.6 # Availability[可用性]
sigma <- 30 # Half-normal shape parameter[半正常形状参数]
J <- length(breaks)-1
y <- array(0, c(R, J, T))
for(i in 1:R) {
M <- rpois(1, lambda) # Individuals within the 1-ha strip[个人在1公顷条]
for(t in 1:T) {
# Distances from point[距点]
d <- runif(M, 0, strip.width)
# Detection process[检测过程]
if(length(d)) {
cp <- phi*exp(-d^2 / (2 * sigma^2)) # half-normal w/ g(0)<1[半正常瓦特/克（0）<1]
d <- d[rbinom(length(d), 1, cp) == 1]
y[i,,t] <- table(cut(d, breaks, include.lowest=TRUE))
}
}
}
y <- matrix(y, nrow=R) # convert array to matrix[将数组矩阵]
# Organize data[组织数据]
umf <- unmarkedFrameGDS(y = y, survey="line", unitsIn="m",
dist.breaks=breaks, tlength=rep(transect.length, R), numPrimary=T)
summary(umf)
# Fit the model[拟合模型]
m1 <- gdistsamp(~1, ~1, ~1, umf, output="density")
summary(m1)
backTransform(m1, type="lambda")
backTransform(m1, type="phi")
backTransform(m1, type="det")
# Empirical Bayes estimates of abundance at each site[在每个站点上的丰富经验Bayes估计]
re <- ranef(m1)
plot(re, layout=c(10,5), xlim=c(-1, 20))
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 13:23:40
