secr.model(secr)
secr.model()所属R语言包：secr
Spatially Explicit Capture–Recapture Models
空间显式捕获 - 再捕获模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A family of capture–recapture models (e.g. SECR) may include submodels that constrain variation in core parameters and include the effects of covariates. The language of generalised linear models is convenient for describing submodels (e.g., Huggins 1989, Lebreton et al. 1992). Each parameter is treated as a linear combination of effects on its transformed ("link") scale. This is useful for combining effects because, given a suitable link function, any combination maps to a feasible value of the parameter. The logit scale has this property for probabilities in (0,1), and the natural log scale works for positive parameters i.e. (0, +Inf).
捕获 - 再捕获模型（例如SECR）的家庭，包括子模型限制在核心参数的变化，包括协变量的影响。广义线性模型是方便的语言描述子模型（例如，哈金斯1989年，勒布雷顿等人，1992）。每个参数被视为它的转化（“链接”）规模的线性组合的效果。因为，给定一个合适的链接功能，任意组合映射到一个可行的参数的值，这是非常有用的，用于组合效果。 logit的规模有此属性的概率在（0,1），规模为正的参数，即（0，+ INF）的自然对数。
Submodels for spatially explicit capture–recapture in secr are defined symbolically using the R formula notation. A separate linear predictor is used for each core parameter. Core parameters are "real" parameters in the terminology of MARK, and secr uses that term to reduce confusion. Four real parameters are commonly modelled in secr: D (density), g0, sigma and z. Only the last three real parameters, the ones jointly defining detection probability as a function of location, can be estimated directly when the model is fitted by maximizing the conditional likelihood. D is then a derived parameter. "z" is a shape parameter used only when the detection function requires three parameters. Other real parameters are used for acoustic models (beta0, beta1; ../doc/secr-sound.pdf) and for the mixture proportion (pmix) in finite mixture models (../doc/secr-finitemixtures.pdf).
子模型在空间上的明确捕获 - 再捕获在secr是用R公式符号定义的象征。一个单独的线性预测用于每个核心参数。核心参数在MARK的术语是“真正”的参数，和secr使用该术语，以减少混乱。 4个实际参数建模secr：D（密度），G0，σ和z。只有最后的三个实参数，共同限定作为位置的函数的检测概率的那些，可以直接估计模型时嵌合通过最大化的条件的可能性。然后，D是派生的参数。 z是仅用于当检测函数需要三个参数的形状参数。其他实际参数是用于声学模型（beta0，β1; / DOC /秘书服务，sound.pdf），并在有限混合模型（。/ DOC /秘书服务，finitemixtures.pdf），的混合物的比例（pmix）。
Each real parameter has a linear predictor of the form
每个参数形式的线性预测
y = X * beta,
Y = X *β，
where y is vector of parameter values on the link scale, X is a design matrix of predictor values, beta is a vector of coefficients, and "*" stands for matrix multiplication. The elements of beta are estimated when we fit the model; in MARK these are called "beta parameters" to distinguish them from the "real" parameter values in y. X has one column for each element of beta. To repeat: there is an X and a beta for each real parameter; elsewhere in the documentation we use "beta" to refer to the vector got by concatenating all the parameter-specific beta's. We now describe design matrices in more detail.
其中，y是向量的链接规模上的参数值，预测值X是一个设计矩阵，β是一个向量的系数，和*代表矩阵乘法。符合模型的元素测试时，我们估计，MARK，这些被称为“测试参数”，以区别于在y真正的参数值。 X有一列中的每个元素的测试。重复：有一个X和一个测试版的每一个真实的参数，其他地方的文档中，我们使用测试版，是指通过连接所有的具体参数测试版的矢量得到了。现在我们更详细描述设计矩阵。
[Some variations on the basic SECR model do not fit easily into this framework. An example is the choice of detection function (halfnormal vs hazard-rate). These are treated as higher-level choices.]
一些变化的基本SECR模型不适合很容易到这个框架中。一个例子是检测功能（halfnormal与危险率）的选择。这些被视为更高级别的选择。]
Design matrices
设计矩阵
The design matrix contains a column of "1"s (for the constant or intercept term) and additional columns as needed to describe the effects in the submodel. Depending on the model, these may be continuous predictors (e.g. air temperature to predict occasion-to-occasion variation in g0), indicator variables (e.g. 1 if animal i was caught before occasion s, 0 otherwise), or coded factor levels.
设计矩阵包含一列的1（常数或截距项），需要额外的列描述子模型的影响。根据该模型，这些可能是连续预测变量（如空气温度预测场合场合变化G0），指标变量（动物例如：1，如果我被抓之前的场合，否则为0），或编码因子水平。
Within secr.fit, a design matrix is constructed automatically from the input data (capthist) and the model formula (e.g. model$g0) in a 2-stage process. First, a data frame is built containing "design data" with one column for each variable in the formula. Second, the R function model.matrix() is used to construct the design matrix. This process is hidden from the user. The design matrix will have at least one more column than the design data, and more if the formula includes interactions or factors with more than two levels. For a good description of the general approach see the documentation for RMark (Laake and Rexstad 2008). The key point is that the necessary design data can be either extracted from the inputs (capthist and mask) or generated automatically (e.g. indicator of previous capture, mentioned in the previous paragraph).
以内secr.fit，设计矩阵构造自动从输入数据（capthist）和模型公式（如model$g0）中的2 - 阶段的过程。首先，建立一个数据框包含的设计数据，在公式中每个变量的一列。二，R的功能model.matrix()被用来构造设计矩阵。这个过程是对用户隐藏的。设计矩阵将有至少一个或多个列比的设计数据，和式包括更多的，如果有两个以上的层次的相互作用或因素。对于一般的做法的一个很好的说明，请参阅文档为RMark（2008年Laake和Rexstad）。关键的一点是必要的设计数据可以提取从输入（capthist和mask）或自动生成的（例如以前的捕获指标，上段中提到的）。
Real parameters fall into two groups: density (D) and detection (g0, sigma and z). Density and detection parameters are subject to different types of effect, so they use different design matrices and are described separately here secr detection models and here secr density models.
实参数分为两组：密度（D）和检测（为g0，西格玛和z）。密度和检测参数不同类型的效果，所以他们使用不同的设计矩阵，并分别描述secr detection models和secr density models。
注意----------Note----------
The structure of secr precludes certain types of model. Unlike density, detection parameters (g0, sigma etc.) cannot be modelled as varying in space per se, whether continuously or discretely (e.g. as a function of habitat class). However, such variation may be modelled between detectors or between sessions. As an example, consider a measure of vegetation cover in a 50-m circle centred on each detector. This may be used as a detector covariate in models for g0 or sigma. A "detector-centred" view of habitat effects is almost as sensible as an "animal-centred" view; the one reservation is that the spatial scale (radius of the circle) is arbitrary rather than being driven by sigma as you might like. Perhaps this could be fixed in future versions by computing the trap covariate "on the fly" from covariates in the habitat mask, given the current magnitude of sigma.
的结构secr排除某些类型的模型。与密度，不同的检测参数（为g0，西格玛等）不能在空间中变化的本身，不论是连续地或离散（例如，作为一个功能的栖息地类）进行建模。然而，这样的变化可以模拟探测器之间或在会话之间。作为一个例子，考虑一个衡量植被覆盖在每个检测器上的50米的圆心。这可以用来作为检测器协变量模型g0或西格玛。作为“动物为本”的A探测器为中心“的观点栖息地的影响几乎是明智的，是一个保留的空间尺度（圆的半径）是任意的，而不是由Sigma驱动，你可能等等。或许，这可以固定在未来的版本中计算的陷阱协“苍蝇的协变量的栖息地屏蔽，电流的大小的标准差。
参考文献----------References----------
approach to building linear models in MARK. In: Cooch, E. and White, G. (eds) Program MARK: A Gentle Introduction. 6th edition. Available online at http://www.phidot.org.
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。
|
发表于 2012-9-30 00:04:35
