secr.model.detection(secr)
secr.model.detection()所属R语言包：secr
Models for Detection Parameters
检测参数模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For spatially explicit capture–recapture estimation of a closed population, we model the detection of individual i on occasion s at detector k. Given n observed individuals on S occasions at K detectors there are therefore n.S.K detection probabilities of interest. We can think of these as elements of a 3-dimensional array. Strictly, we are also interested in the detection probabilities of unobserved individuals, but these are estimated only by extrapolation from those observed so we do not consider them in the array.
对于空间明确的捕获 - 再捕获一个封闭的人口估计，我们模拟检测个人i上一次s在检测k。由于n观察到个人S场合K探测器，因此n.S.K的检测概率的利益。我们可以把这些作为一个3维数组的元素。严格来说，我们也有兴趣在未观察到个人的检测概率，但这些估计只有用外推法所观察到的，所以我们不考虑它们在数组中。
In a null (constant) model, all n.S.K detection probabilities are the same. The conventional sources of variation in capture probability (Otis et al. 1978) appear as variation in the n dimension ('individual heterogeneity' h), in the S dimension ("time variation" t) or as a particular interaction in these two dimensions ('behavioural response to capture' b). Combined effects are possible.
在一个空的（常量）模型，所有的n.S.K的检测概率是相同的。传统的来源变化的捕获概率（奥的斯等人，1978）作为n尺寸（个体异质性H）的变化，出现在S尺寸（随时间的变化吨）在这两个维度（行为反应捕捉到B）作为一种特殊的互动。的复合作用是可能的。
Spatially explicit capture–recapture introduces two sorts of additional complexity. Firstly, detection probability is no longer a scalar (even for a particular animal, occasion and detector combination); it is described by the detection function, which may have two parameters (e.g. g0, sigma for half-normal), three parameters (e.g. g0, sigma, z for the hazard-rate function), or potentially more.
捕获 - 再捕获空间明确引入了额外的复杂性两类。首先，检测概率不再是一个标量（即使对于一个特定的动物，场合和探测器的组合），它被描述由检测功能，也可以有两个参数（例如为g0，西格玛半正常），三个参数（例如G0，标准差，Z的危险率函数），或者可能更多。
Secondly, many more types of variation are possible. Any of the parameters of the detection function may vary with respect to individual (i), occasion (s) or detector (k). For example, there may be a covariate associated with trap location that influences detection probability.
其次，越来越多的类型的变化是可能的。任何参数的检测功能，可随个人（i）的场合（s）或检测器（k）的。例如，有可能是一个陷阱位置影响检测概率与协变量。
The full design matrix for each detection submodel has one row for each combination of i, s and k (animal, occasion and trap). Allowing a distinct probability for each animal (the 'n' dimension) may seem excessive, as continuous individual-specific covariates are feasible only when a model is fitted by maximizing the conditional likelihood (cf Huggins 1989). However, the full n.S.K array is convenient for coding both group membership (Lebreton et al. 1992, Cooch and White 2008) and experience of capture, even when individual-level heterogeneity cannot be modelled.
每个探测子模型的设计矩阵的每个组合有一排i，s和k（动物，机会和陷阱）。允许不同的概率为每个动物（“n尺寸）看起来似乎有点多余，因为连续的个体特异性的协变量是可行的，只有当模型拟合最大化的条件似然（比照哈金斯1989年）。然而，充分的n.S.K阵列是方便编码捕获两个组成员资格（COOCH勒布雷顿等，1992年，2008年和白色）和经验，即使个人层面的异质性时，就无法被建模。
Variation between "sessions" and between latent classes in a finite mixture adds two further dimensions: in principle there is an n.S.K array for each latent class (classes are numbered 1..M), and an n.S.K.M array for each session (sessions are numbered 1..R). The full design matrix has n.S.K.M.R rows. We do not expand on this here.
会话之间以及在一个有限的混合物的潜类之间的变化进一步增加了两个尺寸：在原理上有一个n.S.K阵列的每个潜类（类被编号为1 ..M），和一个n.S.K.M阵列的每个会话（会话的编号为1 ..R“）。完整的设计矩阵n.S.K.M.R行。在这里，我们不扩大。
指定检测参数的影响---------- Specifying effects on detection parameters----------
Effects on parameters of detection probability are specified with R formulae using standard variable names or named covariates supplied by the user. The formula for each detection parameter (g0, sigma, z) may be constant (~1, the default) or some combination of terms in standard R formula notation (see formula).
ŕ式使用标准的变量名或由用户提供的名为协变量指定的参数的检测概率的影响。各检测参数的计算公式为（G0，标准差，Z）是常数（~1，默认）或一些标准的R公式符号的组合（见formula）。
Description
描述
time factor (one level for each occasion)
时间因素（一级每次）
time trend (integer covariate 0 S-1))
时间趋势（整数协变量0：（S-1））
default time covariate
默认情况下，时间协
default trap covariate
默认情况下，陷阱协
learned response
据悉响应
transient (Markovian) response
瞬态（马氏）响应
animal x site learned response
动物X网站了解到响应
animal x site transient response
动物X网站的瞬态响应
site learned response
网站了解到响应
site transient response
现场的瞬态响应
group
组
2-class mixture
2级的混合物
3-class mixture
3级的混合物
session factor (one level for each session)
会话的因素（1为每个会话）
session number 0R-1)
会话编号0（R-1）：
individual covariate
个别协
session covariate
会话协
time covariate
时间协
detector covariate
探测器协
The classic "learned response" is a step change following first detection; this is implemented with the predictor variable "b" which is FALSE up to and including the time of first capture and TRUE afterwards. An alternative is a response that depends only on detection at the last opportunity ("B").
经典的“习得的反应”是一个阶跃变化后，第一次检测，这是实现与预测变量的B是FALSE，包括第一次捕捉和TRUE之后的时间。另一种方法是仅依赖于检测在最后的机会（B）的一个响应。
The site-specific learned and transient responses "bk" and "Bk" imply that an individual becomes trap happy or trap shy in relation to a particular detector, as in the wolverine example of Royle et al. (2011).
该网站特定的“BK”和“BK”的经验教训和瞬态响应意味着，一个人变成了陷阱高兴还是陷阱害羞，关系到一个特定的检测器，在罗伊尔等狼獾的例子。 （2011年）。
Groups ("g") are defined by the interaction of the capthist categorical (factor) individual covariates identified in secr.fit argument "groups". Groups are redundant with conditional likelihood because individual covariates of whatever sort (continuous or categorical) may be included freely in the model.
组（“G”）被定义为capthist分类（因子）的相互作用单个协变量secr.fit参数组确定。什么样的（连续的或绝对的）可能会因为个人的协变量包括在模型中自由组是多余的，有条件的可能性。
Individual heterogeneity ("h" in the notation of Otis et al. 1978) may modelled by treating any detection parameter as a 2-part or 3-part finite mixture e.g. g0 ~ h2. See ../doc/secr-finitemixtures.pdf.
个体异质性（“H”的符号奥的斯等人，1978年），可处理任何检测参数为两部分或三部分组成的有限混合，如模拟G0~ H2。请参阅.. / DOC / SECR的finitemixtures.pdf。
Any other variable name appearing in a formula is assumed to refer to a user-defined predictor. These will be interpreted by searching for name matches in the dataframes of individual, session, time and trap covariates, in that order (remembering that individual covariates other than groups are allowed only when the model is fitted by maximizing the conditional likelihood). The type of the predictor is inferred from the data frame in which it first occurs. Thus if the model included the formula "g0 ~ wetness", and "wetness" was a column in the data frame of time covariates (timecov), then "wetness" would be interpreted as a time covariate, and a column of the same name in covariates(traps) would be ignored. In this case, renaming the column in timecov would expose the traps covariate, and "wetness" would be interpreted as an attribute of detectors, rather than sample intervals. This is a good reason to give covariates distinctive names!
假设在公式中出现的任何其它变量名来指一个用户定义的预测。这些将被解释的个人，会话，时间和陷阱中的dataframes的协变量的名称匹配，通过搜索的顺序（记住以外的其他组的单个协变量的模型只允许在安装最大化的条件的可能性）。推断出的预测的类型是从数据框中，第一次出现的。因此，如果该模型包括公式G0~湿润，和“湿润”是时间的协变量（timecov）的数据框中的一列，然后湿润将被解释为一个时间协变量，和一列的相同名称的协变量（陷阱）将被忽略。在这种情况下，公开的陷阱协的重命名列在timecov，将被解释为“湿润”探测器的属性，而不是采样间隔。这是一个很好的理由给协变量独特的名字！
The design matrix for detection parameters may also be provided manually in the argument dframe. This feature requires some care and is better avoided.
检测参数矩阵的设计也可提供手动参数dframe。此功能需要一定的照顾，能够更好地避免。
The submodels for "g0", "sigma" and "z" are named components of the model argument of secr.fit. They are expressed in R formula notation by appending terms to ~. The name of the response may optionally appear on the left hand side of the formula (e.g. g0~b).
子模型G0，六西格玛到z被命名为model的secr.fit参数的组成部分。他们都表达了在R公式符号通过附加条款~。的反应可任选的名称出现在左手侧的式（例如G0~）。
注意----------Note----------
The parameter "z" was previously called "b"; it was renamed to avoid confusion with the predictor b used in a formula for a learned trap response.
参数“Z”以前被称为B，它被重新命名，以避免与B使用的预测公式中的一个博学多才的陷阱响应。
参考文献----------References----------
Introduction. 6th edition. Available online at http://www.phidot.org.
line-transect method. Biometrics 39, 29–42.
experiments. Biometrika 76, 133–140.
Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62, 67–118.
R. E. (2011) Density estimation in a wolverine population using spatial capture–reecapture models. Journal of Wildlife Management 75, 604–611.
参见----------See Also----------
secr models, secr density models, secr.fit
secr models，secr density models，secr.fit
实例----------Examples----------
## constant (null) model[＃的常数（空）模型]
list(g0 = ~1, sigma = ~1)
## both detection parameters change after first capture[＃两个检测参数改变后第一次捕捉]
list(g0 = ~b, sigma = ~b)
## group-specific parameters; additive time effect on g0[＃组特定的参数;添加剂影响G0]
## groups are defined via the '`groups' argument of secr.fit[＃定义组“组的参数的secr.fit的的通过]
list(g0 = ~ g + t, sigma = ~ g)
## g0 depends on trap-specific covariate[＃G0依赖于特定的陷阱，协]
list(g0 = ~ kcov)
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注：
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发表于 2012-9-30 00:04:27
