distsamp(unmarked)
distsamp()所属R语言包：unmarked
Fit the hierarchical distance sampling model of Royle et al. (2004)
适合距离的分层抽样模型的罗伊尔等。 （2004）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit the hierarchical distance sampling model of Royle et al. (2004) to line or point transect data recorded in discrete distance intervals.
适合距离的分层抽样模型的罗伊尔等。 （2004年），以线或点样在离散距离间隔记录的数据。
用法----------Usage----------
"hazard", "uniform"), output=c("density", "abund"),
unitsOut=c("ha", "kmsq"), starts, method="BFGS", se=TRUE,
engine=c("C", "R"), rel.tol=0.001, ...)
参数----------Arguments----------
参数：formula
Double right-hand formula describing detection covariates followed by abundance covariates. ~1 ~1 would be a null model.
其次是丰富的协变量双击右侧的公式描述检测协变量。 ~1~1。将一个空的模型。
参数：data
object of class unmarkedFrameDS, containing response matrix, covariates, distance interval cut points, survey type ("line" or "point"), transect lengths (for survey = "line"), and units ("m" or "km") for cut points and transect lengths. See example for set up.
类的对象unmarkedFrameDS，响应矩阵，协，间隔距离的切点，测量类型（“线”，“点”），横断面长度（调查=“行”），和单位（“ M“或”公里“）为切点和样带的长度。见范例成立。
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：starts
Vector of starting values for parameters.
向量的参数的初始值。
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：engine
Use code written in C++ or R
使用代码编写的C + +或R
参数：rel.tol
Requested relative accuracy of the integral, see integrate
申请过的积分的相对精度，请参阅integrate
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----------
Unlike conventional distance sampling, which uses the 'conditional on detection' likelihood formulation, this model is based upon the unconditional likelihood and allows for modeling both abundance and detection function parameters.
与常规的距离取样，使用“有条件的检测制订的可能性，根据这种模式是无条件的可能性，并允许进行建模的丰度和检测功能参数。
The latent transect-level abundance distribution f(N | theta) assumed to be Poisson with mean lambda (but see gdistsamp for alternatives).
潜样的丰度分布f(N | theta)假设是泊松平均lambda（但看到gdistsamp的替代品）。
The detection process is modeled as multinomial: y_ij ~ Multinomial(N_i, pi_i1, pi_i2, ..., pi_iJ), where pi_ij is the multinomial cell probability for transect i in distance class j. These are computed based upon a detection function g(x | sigma), such as the half-normal, negative exponential, or hazard rate.
检测过程建模多项：y_ij ~ Multinomial(N_i, pi_i1, pi_i2, ..., pi_iJ)，其中pi_ij多项单元断面我在距离第j类的概率。这些计算根据一个检测函数g(x | sigma)，如半正常，负指数，或危险率。
Parameters lambda and sigma can be vectors
参数lambda和sigma可以是向量
值----------Value----------
unmarkedFitDS object (child class of unmarkedFit-class)
unmarkedFitDS对象（unmarkedFit-class的子类）
注意----------Note----------
You cannot use obsCovs.
您可以不使用obsCovs的。
（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, pp. 1591-1597.
Morrison, S.A. In Press. Hierarchical distance sampling models to estimate population size and habitat-specific abundance of an island endemic. Ecological Applications
参见----------See Also----------
unmarkedFrameDS, unmarkedFit-class fitList, formatDistData, parboot, sight2perpdist, detFuns, gdistsamp, ranef. Also look at vignette("distsamp").
unmarkedFrameDS，unmarkedFit-classfitList，formatDistData，parboot，sight2perpdist，detFuns，gdistsamp，<X >。也看小插曲（“distsamp”）。
实例----------Examples----------
## Line transect examples[＃样线的例子]
data(linetran)
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat),
dist.breaks = c(0, 5, 10, 15, 20),
tlength = linetran$Length * 1000, survey = "line", unitsIn = "m")
})
ltUMF
summary(ltUMF)
hist(ltUMF)
# Half-normal detection function. Density output (log scale). No covariates.[半正常的检测功能。密度输出（对数刻度）。协变量。]
(fm1 <- distsamp(~ 1 ~ 1, ltUMF))
# Some methods to use on fitted model[使用拟合模型的一些方法]
summary(fm1)
backTransform(fm1, type="state") # animals / ha[动物/公顷]
exp(coef(fm1, type="state", altNames=TRUE)) # same[同]
backTransform(fm1, type="det") # half-normal SD[半正常SD]
hist(fm1, xlab="Distance (m)") # Only works when there are no det covars[仅当有没有DET covars]
# Empirical Bayes estimates of posterior distribution for N_i[经验贝叶斯估计的后验分布N_i]
plot(ranef(fm1, K=50))
# Effective strip half-width[有效的带半宽度]
(eshw <- integrate(gxhn, 0, 20, sigma=10.9)$value)
# Detection probability[检测概率]
eshw / 20 # 20 is strip-width[20条宽度]
# Halfnormal. Covariates affecting both density and and detection.[Halfnormal。协变量的影响密度和和检测。]
(fm2 <- distsamp(~area + habitat ~ habitat, ltUMF))
# Hazard-rate detection function.[危险率检测功能。]
(fm3 <- distsamp(~ 1 ~ 1, ltUMF, keyfun="hazard"))
# Plot detection function.[图检测功能。]
fmhz.shape <- exp(coef(fm3, type="det"))
fmhz.scale <- exp(coef(fm3, type="scale"))
plot(function(x) gxhaz(x, shape=fmhz.shape, scale=fmhz.scale), 0, 25,
xlab="Distance (m)", ylab="Detection probability")
## Point transect examples[＃点样的例子]
# Analysis of the Island Scrub-jay data.[分析岛磨砂周杰伦数据。]
# See Sillett et al. (In press)[见Sillett等。 （记者）]
data(issj)
str(issj)
jayumf <- unmarkedFrameDS(y=as.matrix(issj[,1:3]),
siteCovs=data.frame(scale(issj[,c("elevation","forest","chaparral")])),
dist.breaks=c(0,100,200,300), unitsIn="m", survey="point")
(fm1jay <- distsamp(~chaparral ~chaparral, jayumf))
## Not run: [＃不运行：]
data(pointtran)
ptUMF <- with(pointtran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4, dc5),
siteCovs = data.frame(area, habitat),
dist.breaks = seq(0, 25, by=5), survey = "point", unitsIn = "m")
})
# Half-normal.[半正常。]
(fmp1 <- distsamp(~ 1 ~ 1, ptUMF))
hist(fmp1, ylim=c(0, 0.07), xlab="Distance (m)")
# effective radius[有效半径]
sig <- exp(coef(fmp1, type="det"))
ea <- 2*pi * integrate(grhn, 0, 25, sigma=sig)$value # effective area[有效面积]
sqrt(ea / pi) # effective radius[有效半径]
# detection probability[检测概率]
ea / (pi*25^2)
## End(Not run)[＃（不执行）]
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。
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发表于 2012-10-1 13:22:51
