ip.secr(secr)
ip.secr()所属R语言包：secr
Spatially Explicit Capture–Recapture by Inverse Prediction
空间显式捕获 - 再捕获的逆预测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate population density by simulation and inverse prediction (Efford 2004; Efford, Dawson & Robbins 2004). A restricted range of SECR models may be fitted (detection functions with more than 2 parameters are not supported, nor are covariates).
估计人口密度的模拟及反演预测（Efford 2004年Efford，2004年道森和罗宾斯）。可安装范围限制的SECR模型（超过2个参数的检测功能，不支持，也不是协变量）。
用法----------Usage----------
ip.secr (capthist, predictorfn = pfn, predictortype = "null",
detectfn = 0, mask = NULL, start = NULL, boxsize = 0.1,
centre = 3, min.nsim = 10, max.nsim = 2000, CVmax = 0.002,
var.nsim = 1000, maxbox = 5, maxtries = 2, ...)
pfn(capthist, N.estimator)
参数----------Arguments----------
参数：capthist
capthist object including capture data and detector (trap) layout
capthist对象包括采集数据和检测器（陷阱）布局
参数：predictorfn
a function with two arguments (the first a capthist object) that returns a vector of predictor values
（的第一个capthist的对象）有两个参数，返回一个向量的预测值的功能
参数：predictortype
value (usually character) passed as the second argument of predictorfn
值（通常为字符）的第二个参数传递的predictorfn
参数：detectfn
integer code or character string for shape of detection function 0 halfnormal, 2 exponential, 3 uniform) – see detectfn
的整数代码或字符串形状的检测功能，2 0 halfnormal指数，3均匀） - detectfn
参数：mask
optional habitat mask to limit simulated population
可选的栖息地屏蔽，以限制模拟人口
参数：start
vector of np initial parameter values (density, g0 and sigma)
矢量NP的初始参数值（密度，g0和SIGMA）
参数：boxsize
scalar or vector of length np for size of design as fraction of central parameter value
部分中央参数值的标量或矢量的大小设计长度为NP
参数：centre
number of centre points in simulation design
在模拟设计的中心点
参数：min.nsim
minimum number of simulations per point
每点的最小数目的模拟
参数：max.nsim
maximum number of simulations per point
模拟每点的最大数量
参数：CVmax
tolerance for precision of points in predictor space
在预测误差的精密点空间
参数：var.nsim
number of additional simulations to estimate variance-covariance matrix
一些额外的模拟估计方差 - 协方差矩阵
参数：maxbox
maximum number of attempts to "frame" solution
“框架”解决方案的最大尝试次数
参数：maxtries
maximum number of attempts at each simulation
在每个仿真的最大尝试次数
参数：...
further arguments passed to sim.popn
进一步的论据传递到sim.popn的
参数：N.estimator
character value indicating population estimator to use
字符值，该值指示人口估计使用
Details
详细信息----------Details----------
"Inverse prediction" uses methods from multivariate calibration (Brown 1982). The goal is to estimate population density (D) and the parameters of a detection function (usually g0 and sigma) by "matching" statistics from predictorfn(capthist) (the target vector) and statistics from simulations of a 2-D population using the postulated detection model. Statistics (see Note) are defined by the predictor function, which should return a vector equal in length to the number of parameters (np = 3). Simulations of the 2-D population use sim.popn. The simulated population is sampled with sim.capthist according to the detector type (e.g., "single" or "multi") and detector layout specified in traps(capthist).
... may be used to control aspects of the simulation by passing named arguments (other than D) to sim.popn. The most important arguments of sim.popn to keep an eye on are "buffer" and "Ndist". "buffer" defines the region over which animals are simulated (unless mask is specified) - the region should be large enough to encompass all animals that might be caught. "Ndist" controls the number of individuals simulated within the buffered or masked area. The default is "poisson". Use "Ndist = fixed" to fix the number in the buffered or masked area A at N = DA. This conditioning reduces the estimated standard error of D-hat, but conditioning is not always justified - seek advice from a statistician if you are unsure.
...可用于通过命名的参数（D以外）sim.popn控制方面的仿真。最重要的参数sim.popn留意是“缓冲”和“Ndist”。 “缓冲”的动物模拟（除非mask指定）定义的区域 - 该区域应足够大，以涵盖所有可能会被捕获的动物。 “Ndist控制的模拟在缓冲或屏蔽区域的个人。默认值是“泊”。使用“Ndist =固定”固定的数量在缓冲或屏蔽的区域AN = DA。这降低了估计标准误差D-hat空调，但空调并不总是合理的 - 如果你不确定，寻求一个统计学家的意见。
The simulated 2-D distribution of animals is Poisson by default. There is no "even" option as in Density.
2-D模拟动物的分布是泊松默认情况下。有没有甚至选项中密度。
Simulations are conducted on a factorial experimental design in parameter space - i.e. at the vertices of a cuboid "box" centred on the working values of the parameters, plus an optional number of centre points. The size of the "box" is specified as a fraction of the working values, so for example the limits on the density axis are D*(1–boxsize) and D*(1+boxsize) where D* is the working value of D. For g0, this computation uses the odds transformation (g0/(1–g0)). boxsize may be a vector defining different scaling on each parameter dimension.
在参数空间中 - 即在一个长方体的“盒子”为中心的工作的参数值，以及可选的中心点数目的顶点上的析因实验设计进行模拟。的大小的“盒子”被指定为工作值的一小部分，所以例如密度轴线上的限制是D *（1-boxsize）和D *（1 + boxsize）其中D *是工作值D.对于G0，该计算使用的比值变换（G0 /（1-G0））。 boxsize可能是一个定义每个参数尺寸不同的缩放矢量。
A multivariate linear model is fitted to predict each set of simulated statistics from the known parameter values. The number of simulations at each design point is increased (doubled) until the residual standard error divided by the central value is less than CVmax for all parameters. An error occurs if max.nsim is exceeded.
多元线性模型拟合预测每一套模拟的统计，从已知的参数值。模拟在每个设计点的是，增加的数目（加倍），直到剩余标准误差除以中央值是小于CVmax为所有参数。错误发生，如果超过max.nsim。
Once a model with sufficient precision has been obtained, a new working vector of parameter estimates is "predicted" by inverting the linear model and applying it to the target vector. A working vector is accepted as the final estimate when it lies within the box; this reduces the bias from using a linear approximation to extrapolate a nonlinear function. If the working vector lies outside the box then a new design is centred on value for each parameter in the working vector.
一旦模型已获得足够的精度，参数估计值的一个新的工作向量是“预测”通过反转的线性模型，将其应用到目标向量。一个工作的向量被接受作为最终的估计值，它位于内盒时，从使用的线性近似来推断的一个非线性函数，这时间偏置。如果你的工作矢量位于外包装箱，然后一个新的设计中心工作矢量中的每个参数值。
Once a final estimate is accepted, further simulations are conducted to estimate the variance-covariance matrix. These also provide a parametric bootstrap sample to evaluate possible bias. Set var.nsim = 0 to suppress the variance step.
一旦有了最终的预算被接受，进行进一步的仿真估算的方差 - 协方差矩阵。还提供了一个参数bootstrap样本，以评估可能的偏差。 var.nsim = 0，抑制变异步骤。
See Efford et al. (2004) for another description of the method, and Efford et al. (2005) for an application.
见Efford等。 （2004）的方法，另一种描述和Efford等。 （2005年）的应用程序。
The value of predictortype is passed as the second argument of the chosen predictorfn. By default this is pfn, for which the second argument (N.estimator) is a character value from c("n", "null","zippin","jackknife"), corresponding respectively to the number of individuals caught (Mt+1), and N-hat from models M0, Mh and Mb of Otis et al. (1978).
作为第二个参数所选择的predictortypepredictorfn通过。默认情况下，这是pfn，而第二个参数（N.estimator）是一个字符值从c（“N”，“空”，“痛饮”，“刀切法”），分别对应于个人抓到的数量（百万吨+1），和N-hat模型M0，Mh和MB奥的斯等人。 （1978）。
If not provided, the starting values are determined automatically with autoini.
如果没有提供，初始值，自动确定autoini。
Linear measurements are assumed to be in metres and density in animals per hectare (10 000 m^2).
线性测量都被假定为在动物中每公顷的米，密度（10 000m^2）。
值----------Value----------
For ip.secr, a list comprising
对于ip.secr，列表中包括
参数：call
the function call
的函数调用
参数：IP
dataframe with estimated density /ha, g0 and sigma (m)
数据框与密度估计/ha，G0和sigma（M）
参数：vcov
variance-covariance matrix of estimates
方差 - 协方差矩阵的估计
参数：ip.nsim
total number of simulations
总数量的模拟
参数：variance.bootstrap
dataframe summarising simulations for variance estimation
数据框的总结性的模拟方差估计
参数：proctime
processor time (seconds)
处理器时间（秒）
For pfn, a vector of numeric values corresponding to N-hat, p-hat, and RPSV, a measure of the spatial scale of individual detections.
对于pfn，N-hat对应的数字值的向量，p-hat和RPSV，衡量个人的空间尺度检测。
警告----------Warning----------
Simulation becomes unreliable with very sparse populations, or sparse sampling, because some simulated datasets will have no recaptures or even no captures. Adjustments were made in secr 2.3.1 to make the function more stable in these conditions (e.g., allowing a failed simulation to be repeated, by setting the "maxtries" argument > 1), but results probably should not be relied upon when
是稀疏的人群，或稀疏采样，模拟变得不可靠，因为一些模拟数据集将不会夺回甚至没有捕获。进行了调整2.3.1在SECR至功能更稳定，在这样的条件下（例如，允许重复失败的模拟，通过设置“maxtries”的说法> 1），但结果可能不应该依赖时
注意----------Note----------
Each statistic is expected to have a monotonic relationship with one parameter when the other parameters are held constant. Typical statistics are -
每一个统计，预计将有一个参数时，其他参数保持不变的单调性关系。典型的统计数据 -
where N-hat and p-hat are estimates of population size and capture probability from the naive application of a nonspatial population estimator, and RPSV is a trap-revealed measure of the scale of movement.
N-hat和p-hat是人口规模的估计，从天真的应用程序的非空间人口估计的捕获概率，和RPSV是一个陷阱启示措施的规模的运动。
This method provides nearly unbiased estimates of the detection parameter g0 when data are from single-catch traps (likelihood-based estimates of g0 are biased in this case - Efford, Borchers & Byrom 2009).
这种方法提供了几乎无偏估计的数据时检测参数G0单赶上的陷阱（G0偏向在这种情况下，基于可能性的估计 - Efford，BORCHERS＆拜罗姆2009年）。
The implementation largely follows that in Density, and it may help to consult the Density online help. There are some differences: the M0 and Mb estimates of population-size in ip.secr can take non-integer values; the simulation design used by ip.secr uses odds(g0) rather than g0; the default boxsize and CVmax differ from those in Density 4.4. There is no provision in ip.secr for two-phase estimation, using a different experimental design at the second phase. If you wish you can achieve the same effect by using the estimates as starting values for a second call of ip.secr (see examples).
的实施在很大程度上如下密度，它可以帮助咨询密度的在线帮助。也有一些不同之处：M0和Mb的人口规模估计在ip.secr可以采取非整数值; ip.secr使用的可能性，而不是G0（G0），默认的boxsize和使用的仿真设计CVmax不同密度4.4。有没有规定在ip.secr两阶段估计，在第二阶段使用不同的实验设计。如果你想使用第二个检测的ip.secr（参见示例）的初始值估计可以达到同样的效果。
Maximum likelihood estimates from secr.fit are preferable in several respects to estimates from inverse prediction (speed*; more complex models; tools for model selection). ip.secr is provided for checking estimates of g0 from single-catch traps, and for historical continuity.
最大似然估计secr.fit是最好的逆预测估计在以下几个方面（速度，更复杂的模型，模型选择的工具）。 ip.secr提供检查估计G0从单一渔获物的陷阱，和对历史的延续性。
* autoini with thin = 1 provides fast estimates from a simple halfnormal model if variances are not required.
*autoini薄= 1提供了快速的估计从一个简单的halfnormal的模型如果差异并不需要。
参考文献----------References----------
Statistical Society, Series B 44, 287–321.
Oikos 106, 598–610.
by spatially explicit capture–recapture: likelihood-based methods. In: D. L. Thompson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer. Pp. 255–269.
for analysing capture-recapture data from passive detector arrays. Animal Biodiversity and Conservation 27, 217–228.
field test of two methods for density estimation. Wildlife Society Bulletin 33, 731–738.
Statistical inference from capture data on closed animal populations. Wildlife Monographs 62.
参见----------See Also----------
capthist, secr.fit, RPSV, autoini, sim.popn, detection functions
capthist，secr.fit，RPSV，autoini，sim.popn，detection functions
实例----------Examples----------
## Not run: [＃不运行：]
## these calculations may take several minutes[＃这些计算可能需要几分钟]
## default settings[＃默认设置]
ip.secr (captdata)
## coarse initial fit, no variance step[＃粗初始合适，没有差异步骤]
ip1 <- ip.secr (captdata, boxsize = 0.2, CVmax=0.01, var=0)
## refined fit[＃精炼适合]
ip2 <- ip.secr (captdata, start = ip1$IP[,"estimate"],
boxsize = 0.1, CVmax=0.002, var=1000)
ip2
## compare to MLE of same data using multi-catch assumption[＃比较多捕捉假设使用相同的数据极大似然估计]
predict(secrdemo.0)
## improvise another predictor function (dbar instead of RPSV)[＃即兴另一个预测功能（DBAR，而不是RPSV）]
pfn2 <- function (capthist, v) { ## v is not used[＃v的不使用]
sumni <- sum(capthist!=0) ## total detections[＃总检测]
n <- nrow(capthist) ## number of individuals[＃号的个人]
nocc <- ncol(capthist) ## number of occasions[＃多个场合]
c(N = n, p = sumni/n/nocc, dbar = dbar(capthist))
}
ip.secr (captdata, predictorfn = pfn2)
## 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。
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。
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发表于 2012-9-29 23:58:32
