expected.n(secr)
expected.n()所属R语言包：secr
Expected Number of Individuals
预计的个体数量
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the expected number of individuals detected across a detector layout or at each cluster of detectors.
在一个探测器布局，在每个聚类的探测器检测到的个人计算预期。
用法----------Usage----------
expected.n(object, session = NULL, group = NULL, bycluster
= FALSE, splitmask = FALSE)
参数----------Arguments----------
参数：object
secr object output from secr.fit
secr对象输出secr.fit
参数：session
character session vector
字符会话矢量的
参数：group
group – for future use
组 - 为将来使用
参数：bycluster
logical to output the expected number for clusters of detectors rather than whole array
逻辑输出预期为聚类的探测器数量，而不是整个阵列
参数：splitmask
logical for computation method (see Details)
逻辑计算方法（见详情）
Details
详细信息----------Details----------
The expected number of individuals detected is E(n) = integral p.(X) D(X) dX where the integration is a summation over object$mask. p.(X) is the probability an individual at X will be detected at least once either on the whole detector layout (bycluster = FALSE) or on the detectors in a single cluster (see pdot for more on p.). D(X) is the expected density at X, given the model. D(X) is constant (i.e. density surface flat) if object$CL == TRUE or object$model$D == ~1, and for some other possible models.
预期数量的个体中，检测是整合E(n) = integral p.(X) D(X) dX其中是一个求和object$mask的。 p.(X)的概率是个人X至少一次检测，无论是在整个探测器的布局（bycluster = FALSE）或在一个聚类探测器（见PDOT更多 p.“）。 D(X)是在X预期的密度，给出的模型。 D(X)是恒定的（即密度表面平整），如果object$CL == TRUE或object$model$D == ~1，和其他一些可能的模式。
If the bycluster option is selected and detectors are not, in fact, assigned to clusters then each detector will be treated as a cluster, with a warning.
如果bycluster选项被选中，探测器都没有，事实上，分配到聚类的每个探测器将被视为一个聚类，一个警告。
By default, a full habitat mask is used for each cluster. This is the more robust option. Alternatively, the mask may be split into subregions defined by the cells closest to each cluster.
默认情况下，一个完整的栖息地子网掩码用于为每个聚类。这是更强大的选项。或者可被分成由最接近每个聚类的单元定义的分区域，掩模。
The calculation takes account of any fitted continuous model for spatial variation in density (note Warning).
任何装有密度的空间变化（附注警告）连续型的计算需要考虑。
值----------Value----------
The expected count (bycluster = FALSE) or a vector of expected counts, one per cluster. For multi-session data, a list of such vectors.
预期的计数（bycluster = FALSE）或预期的数量，每个聚类的向量。对于多会话数据，这种向量的列表。
警告----------Warning----------
This function changed slightly between 2.1.0 and 2.1.1, and now performs as indicated here when bycluster = TRUE and clusters are not specified.
这细微的变化2.1.0和2.1.1的功能，现在进行时，此处显示bycluster = TRUE，聚类没有指定。
Detectors are assumed to be independent (as with detector types "proximity", "count" etc.). The computed E(n) does not apply when there is competition among detectors, e.g., when detector = "multi".
探测器被假设为是独立的（如探测器类型的“接近”，“计数”等）。计算E（N）时，并不适用于有检测器之间的竞争，例如，当探测器=多。
The prediction of density at present considers only the base level of density covariates, such as cell-specific habitat variables.
目前密度的预测只考虑密度协变量的基本水平，如单元的特定栖息地变量。
参见----------See Also----------
region.N
region.N
实例----------Examples----------
expected.n(secrdemo.0)
## Not run: [＃不运行：]
expected.n(secrdemo.0, bycluster = TRUE)
expected.n(ovenbird.model.D)
## Clustered design[＃聚类设计]
mini <- make.grid(nx = 3, ny = 3, spacing = 50, detector =
"proximity")
tempgrids <- trap.builder (cluster = mini , method = "all",
frame = expand.grid(x = seq(1000, 9000, 2000),
y = seq(1000, 9000, 2000)), plt = TRUE)
capt <- sim.capthist(tempgrids, popn = list(D = 2))
tempmask <- make.mask(tempgrids, buffer = 100,
type = "clusterbuffer")
fit <- secr.fit(capt, mask = tempmask, trace = FALSE)
En <- expected.n(fit, bycluster = TRUE)
## GoF or overdispersion statistic[＃GoF的或偏大统计]
p <- length(fit$fit$par)
y <- cluster.counts(capt)
## scaled by n-p[＃由n - 对缩放]
sum((y - En)^2 / En) / (length(En)-p)
sum((y - En)^2 / En) / sum(y/En)
## 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。
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 2012-9-29 23:57:41
