region.N(secr)
region.N()所属R语言包：secr
Population Size
人口规模
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate the expected and realised populations in a region, using a fitted spatially explicit capture–recapture model. Density is assumed to follow an inhomogeneous Poisson process in two dimensions. Expected N is the volume under a fitted density surface; realised N is the number of individuals within the region for the current realisation of the process (cf Johnson et al. 2010; see
预期，实现了人口估计在一个区域，一个厨房空间明确的捕获 - 再捕获模型。假定密度是遵循非齐次泊松过程在两个方面。预计N是音量下一个装密度的表面;实现N是目前实现的过程（参见约翰逊等人2010年在该区域的个人
用法----------Usage----------
region.N (object, region = NULL, spacing = NULL, session = NULL,
group = NULL, se.N = TRUE, alpha = 0.05, loginterval = TRUE,
keep.region = FALSE, nlowerbound = TRUE, RN.method = 'poisson')
参数----------Arguments----------
参数：object
secr object output from secr.fit
secr对象输出secr.fit
参数：region
mask object defining the possibly non-contiguous region for which population size is required, or vector polygon(s) (see Details)
口罩对象定义可能是不连续的区域的人口规模是必需的，或矢量多边形（S）（见详情）
参数：spacing
spacing between grid points (metres) if region mask is constructed on the fly
网格点（米）之间的间距，如果区域的掩模上的苍蝇构造
参数：session
character session
字符会话
参数：group
group – for future use
组 - 为将来使用
参数：se.N
logical for whether to estimate SE(N-hat) and confidence interval
逻辑估计SE（N-hat）和置信区间
参数：alpha
alpha level for confidence intervals
α水平置信区间
参数：loginterval
logical for whether to base interval on log(N)
符合逻辑的，是否基于log的时间间隔（N）
参数：keep.region
logical for whether to save the raster region
逻辑是否保存光栅区域
参数：nlowerbound
logical for whether to use n as lower bound when computing log interval for realised N
逻辑实现是否使用n下限计算log的时间间隔时，Ň
参数：RN.method
character string for method used to calculate realised N (RN) and its sampling variance. "poisson" or "MSPE".
字符的字符串的方法来计算实现N（RN），其抽样误差。 “泊”或“MSPE”。
Details
详细信息----------Details----------
If the density surface of the fitted model is flat (i.e. object$model$D == ~1 or object$CL == TRUE) then E(N) is simply the density multiplied by the area of region, and the standard error is also a simple product. In the conditional likelihood case, the density and standard error are obtained by first calling derived.
如果的密度拟合模型表面是平的，（即object$model$D == ~1或object$CL == TRUE）然后E(N)是简单的的密度乘以的面积region，和标准错误一个简单的产品。在的可能性的条件的情况下，密度和标准错误被首先调用derived通过以下方式获得。
If, on the other hand, the density has been modelled then the density surface is predicted at each point in region and E(N) is obtained by discrete summation. Pixel size may have a minor effect on the result - check by varying spacing. Sampling variance is determined by the delta method, using a numerical approximation to the gradient of E(N) with respect to each beta parameter.
如果，在另一方面，密度被建模，然后预测的密度表面在每个点在region和E(N)是通过以下方式获得离散求和。像素大小可能有轻微影响的结果 - 检查由不同spacing。抽样方差确定通过的增量的方法，相对于每一个测试参数使用的数值近似的梯度E(N)。
The region may be defined as a mask object (if omitted, the mask component of object will be used). Alternatively, region may be a SpatialPolygonsDataFrame object (see package sp), and a raster mask will be constructed on the fly using the specified spacing. See make.mask for an example importing a shapefile to a SpatialPolygonsDataFrame.
该区域可以被定义作为掩模对象（如果省略，掩模组件object将被使用）。还有一种方法，region可能是一个SpatialPolygonsDataFrame对象（见套件sp），和一个光栅掩模，将构成在飞行中，使用指定的间距。见make.mask的一个例子导入shapefile的一个SpatialPolygonsDataFrame。
Note: The option of specifying a polygon rather than a mask for region does not work if the density model in object uses spatial covariates: these must be passed in a mask.
注意：该选项指定一个多边形的，而不是一个面具region不工作，如果在object密度模型使用空间的协变量：必须通过在这些面具。
Group-specific N has yet to be implemented.
组特定的N还没有得到落实。
Population size is adjusted automatically for the number of clusters in "mashed" models (see mash). However, the population size reported is that associated with a single cluster unless regionmask is specified.
人口规模的数字聚类在“泥”模式的自动调整（见mash）。然而，人口规模报道的是，有一个聚类除非regionmask指定有关。
值----------Value----------
If se.N = FALSE, the numeric value of expected population size, otherwise, a dataframe with rows "E.N" and "R.N", and columns as below.
如果se.N = FALSE，预期人口规模的数值，否则，一个数据框行“E.N”和“R.N”，和列如下。
For multiple sessions, the value is a list with one component per session, each component as above.
多个会话，该值是一个列表，每节的一个组成部分，每一个部分，上面。
If keep.region = TRUE then the mask object for the region is saved as the attribute "region" (see Examples).
如果keep.region = TRUE那么该区域的遮罩对象保存为“区域”的属性（见例）。
注意----------Note----------
The estimates of expected and realised N are generally very similar, or identical, but realised N usually has lower estimated variance, especially if the n detected animals comprise a large fraction.
的估计的预期和实现N通常是非常相似的，或相同的，但实现N通常具有较低的估计方差，尤其是如果n检测到的动物包括一个大分数。
Realised N is given by R(N) = n + integral_over_B (1 - p.(X))D(X) dX (the second term represents undetected animals). This definition strictly holds only when region B is at least as large as the region of integration used to fit the model; only with this condition can we be sure all n detected animals have centres within B. The sampling variance of R(N), technically a mean square prediction error (Johnson et al. 2010), is approximated by summing the expected Poisson variance of the true number of undetected animals and a delta-method estimate of its sampling variance, obtained as for E(N).
变现N的R(N) = n + integral_over_B (1 - p.(X))D(X) dX（第二项代表未被发现的动物）。这个定义严格持有B区是在至少一样大的区域整合的模式，以适应使用时，这种情况下，我们可以确保所有n检测到的动物有中心内B.抽样误差R(N)，技术上均方预测误差（Johnson等，2010），通过求和，获得预期泊松方差未检测到的动物的真实数量和一个delta-方法估计其抽样方差为<近似X>。
By default, a shortcut is used to compute the sampling variance of realised N. With this option (RN.method = "poisson") the sampling variance is the sampling variance of E(N) minus the estimate of E(N) (representing Poisson process variance). This has been found to give reliable confidence intervals in simulations (Efford and Fewster 2012).
默认情况下，使用一个快捷方式实现N抽样方差的计算。使用此选项（RN.method =泊松）的抽样误差是抽样方差E(N)减去E(N)（占泊松过程方差的估计）。已发现这在的模拟（Efford和Fewster 2012），得到可靠的置信区间。
If RN.method is neither "MSPE" nor "poisson" (ignoring case) then the estimate of expected N is also used for realised N, and the "poisson" shortcut variance is used.
如果RN.method既不是MSPE也不是泊松（忽略大小写），然后估计预期N也用于实现N，和“泊松快捷的方差使用。
Johnson et al. (2010) use the notation μ(B) for expected N and N(B) for realised N in region B.
Johnson等人。 （2010）使用的符号μ(B)预期N和实现N(B)N的区域B。
In our case, the relative SE (CV) of μ(B) is the same as that for the estimated density D if D has been estimated using the Poisson distribution option in secr.fit or derived(). If D has been estimated with the binomial distribution option, its relative SE for simple models will be the same as that of N(B), assuming that B is the full extent of the original mask.
在我们的例子中，的相对SE（CV）μ(B)是相同的密度估计D如果D已secr.fit估计使用泊松分布选项或derived()。如果D已经估计与二项式分布的选项，其相对SE简单的模型是一样的N(B)，假设B是原面具的充分程度。
参考文献----------References----------
likelihood methods for capture–recapture studies. Biometrics 64, 377–385.
explicit capture–recapture. Oikos (In review)
approach for making ecological inference from distance sampling data. Biometrics 66, 310–318.
参见----------See Also----------
secr.fit, derived, make.mask, expected.n, closedN
secr.fit，derived，make.mask，expected.n，closedN
实例----------Examples----------
region.N(secrdemo.0)
## Not run: [＃不运行：]
## a couple more routine examples[＃一对夫妇更多的日常例子]
region.N(secrdemo.CL)
region.N(ovenbird.model.D)
## region defined as vector polygon[＃矢量多边形区域定义为]
## retain and plot region mask[＃保留并绘制区域的掩模]
temp <- region.N(possum.model.0, possumarea, spacing = 40,
keep.region = TRUE)
temp
plot (attr(temp, "region"), type = 'l')
## 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 2012-9-30 00:03:27
