derived(secr)
derived()所属R语言包：secr
Derived Parameters of Fitted SECR Model
导出参数拟合SECR模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute derived parameters of spatially explicit capture-recapture model. Density is a derived parameter when a model is fitted by maximizing the conditional likelihood. So also is the effective sampling area (in the sense of Borchers and Efford 2008).
计算派生的空间明确的捕获 - 再捕获模型的参数。密度是一个派生的参数，当模型拟合的条件的可能性最大化。因此，也是有效采样区域（在这个意义上博彻斯和Efford 2008）。
用法----------Usage----------
derived(object, sessnum = NULL, groups = NULL, alpha = 0.05,
se.esa = FALSE, se.D = TRUE, loginterval = TRUE,
distribution = NULL)
esa(object, sessnum = 1, beta = NULL, real = NULL, noccasions = NULL)
参数----------Arguments----------
参数：object
secr object output from secr.fit, or an object of class c("list","secrlist")
secrsecr.fit，或对象类c("list","secrlist")的对象输出
参数：sessnum
index of session in object$capthist for which output required
指数的会话对象capthist的输出需要
参数：groups
vector of covariate names to define group(s) (see Details)
向量的协变量的名称来定义组（S组）（见详情）
参数：alpha
alpha level for confidence intervals
α水平置信区间
参数：se.esa
logical for whether to calculate SE(mean(esa))
逻辑计算SE（意思是（ESA））
参数：se.D
logical for whether to calculate SE(D-hat)
逻辑计算SE（D-帽子）
参数：loginterval
logical for whether to base interval on log(D)
碱基的时间间隔记录是否符合逻辑的（D）
参数：distribution
character string for distribution of the number of individuals detected
字符串的个体中，检测的数量分布
参数：beta
vector of fitted parameters on transformed (link) scale
矢量变换（链接）规模的拟合参数
参数：real
vector of "real" parameters
“真实”的参数向量
参数：noccasions
integer number of sampling occasions (see Details)
整数的采样场合（见详情）
Details
详细信息----------Details----------
The derived estimate of density is a Horvitz-Thompson-like estimate:
派生的密度估计是一个霍维茨 - 汤普森的估计：
where (theta-hat) is the estimate of effective sampling area for animal i with detection parameter vector θ.
(theta-hat)是动物i检测参数向量θ有效取样面积的估计。
A non-null value of the argument distribution overrides the value in object$details. The sampling variance of D-hat from secr.fit by default is spatially unconditional (distribution = "Poisson"). For sampling variance conditional on the population of the habitat mask (and therefore dependent on the mask area), specify distribution = "binomial". The equation for the conditional variance includes a factor (1 - a/A) that disappears in the unconditional (Poisson) variance (Borchers and Efford 2007). Thus the conditional variance is always less than the unconditional variance. The unconditional variance may in turn be an overestimate or (more likely) an underestimate if the true spatial variance is non-Poisson.
一个非空值的参数distribution覆盖的值object$details。的抽样方差的D-hatsecr.fit默认情况下，在空间上是无条件的（distribution = "Poisson"）。条件的栖息地的面具（因此，依赖于遮蔽区域）的人口抽样误差，请指定distribution = "binomial"。的条件方差方程的一个因素(1 - a/A)，消失在无条件方差（泊松）（BORCHERS和Efford 2007）。因此，有条件的方差总是小于无条件方差。无条件方差可能又是一个高估或（更可能）是低估了真正的空间变异是不泊松。
Derived parameters may be estimated for population subclasses (groups) defined by the user with the groups argument. Each named factor in groups should appear in the covariates dataframe of object$capthist (or each of its components, in the case of a multi-session dataset).
可估计为人口的子类（组）与groups参数由用户定义的导出参数。每个命名的因素groups应该出现在协变量数据框的对象capthist美元（或每一个组成部分，在多会话数据集的情况下）。
esa is used by derived to compute individual-specific
esa使用derived计算个体特异性
where p.(X) is the probability an individual at X is detected at least once and the z_i are optional individual covariates. Integration is over the area A of the habitat mask.
其中p.(X)的概率是在X的个人被检测到的至少一次和z_i是可选的个别的协变量。整合是在区域A的栖息地屏蔽的。
The argument noccasions may be used to vary the number of sampling occasions; it works only when detection parameters are constant across individuals and across time.
的参数noccasions可能被用来分配不同数量的采样场合，它的工作原理，只有当检测参数恒定的个人和跨越时间。
The effective sampling area "esa" (a(theta-hat)) reported by derived is equal to the harmonic mean of the a_i (theta-hat) (arithmetic mean prior to version 1.5). The sampling variance of a(theta-hat) is estimated by
有效取样面积欧空局（a(theta-hat)）报告的derived是平等的调和平均值a_i (theta-hat)（算术平均1.5版之前）。 a(theta-hat)的抽样方差估计
where V-hat is the asymptotic estimate of the variance-covariance matrix of the beta detection parameters (theta) and G-hat is a numerical estimate of the gradient of a(theta) with respect to theta, evaluated at theta-hat.
其中V-hat是渐近估计方差 - 协方差矩阵的β检测参数（theta）和G-hat是一个数值估计的梯度a(theta)就theta，theta-hat评估。
A 100(1–alpha)% asymptotic confidence interval is reported for density. By default, this is asymmetric about the estimate because the variance is computed by backtransforming from the log scale. You may also choose a symmetric interval (variance obtained on natural scale).
A 100（1-α）％的渐近置信区间密度的报道。默认情况下，这是不对称的估计值，因为方差通过从log中规模backtransforming的计算。您也可以选择一个对称区间（方差天然鳞片）。
The vector of detection parameters for esa may be specified via beta or real, with the former taking precedence. If neither is provided then the fitted values in object$fit$par are used. Specifying real parameter values bypasses the various linear predictors. Strictly, the "real" parameters are for a naive capture (animal not detected previously).
检测参数向量esa可能会通过指定beta或real，与前优先考虑。如果没有提供的拟合值object$fit$par使用。指定real参数值绕过的各种线性预测。严格来说，真正的参数是一个天真的捕获（没有检测到以前的动物）。
The computation of sampling variances is relatively slow and may be suppressed with se.esa and se.D as desired.
抽样方差的计算是相对缓慢的，并可能抑制se.esa和se.D根据需要，。
值----------Value----------
Dataframe with one row for each derived parameter ("esa", "D") and columns as below
带有一个行的每个派生的参数（ESA，D）和如下的列中的数据框
For a multi-session or multi-group analysis the value is a list with one component for each session and group.
The result will also be a list if object is an "secrlist".
其结果也将是一个列表，如果object是一个“secrlist”。
注意----------Note----------
Before version 2.1, the output table had columns for "varcomp1" (the variance in D-hat due to variation in n, i.e., Huggins' s^2), and "varcomp2" (the variance in D-hat due to uncertainty in estimates of detection parameters).
在2.1版之前，输出表列varcomp1“，（在D-hat由于的变化在n，即Huggins的s^2），和”varcomp2（方差方差D-hat由于检测参数估计的不确定性）。
These quantities are related to CVn and CVa as follows:
这些量CVN和CVA如下：
参考文献----------References----------
参见----------See Also----------
predict.secr, print.secr,
predict.secr，print.secr，
实例----------Examples----------
## Not run: [＃不运行：]
## extract derived parameters from a model fitted previously[＃提取从前面安装一个模型得出的参数]
## by maximizing the conditional likelihood [＃最大化的条件的可能性]
derived (secrdemo.CL)
## what happens when sampling variance is conditional on mask N?[＃会发生什么时，抽样误差是有条件的屏蔽器n？]
derived(secrdemo.CL, distribution = "binomial")
## fitted g0, sigma[＃安装G0，Σ]
esa(secrdemo.CL)
## force different g0, sigma[＃强制执行不同的G0，Σ]
esa(secrdemo.CL, real = c(0.2, 25))
## 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 23:56:25
