predict.secr(secr)
predict.secr()所属R语言包：secr
SECR Model Predictions
SECR模型预测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Evaluate a spatially explicit capture–recapture model. That is, compute the "real" parameters corresponding to the "beta" parameters of a fitted model for arbitrary levels of any variables in the linear predictor.
评估一个直观的捕获 - 再捕获模型。也就是说，计算出相应的真正的参数测试版任意级别的任何变量的线性预测模型的拟合参数。
用法----------Usage----------
## S3 method for class 'secr'
predict(object, newdata = NULL, se.fit = TRUE, alpha = 0.05,
savenew = FALSE, scaled = FALSE, ...)
detectpar (object, ...)
参数----------Arguments----------
参数：object
secr object output from secr.fit
secr对象输出secr.fit
参数：newdata
optional dataframe of values at which to evaluate model
可选的值来评估模型的数据框
参数：se.fit
logical for whether output should include SE and confidence intervals
逻辑输出是否应包括SE和置信区间
参数：alpha
alpha level for confidence intervals
α水平置信区间
参数：savenew
logical for whether newdata should be saved
逻辑newdata应保存
参数：scaled
logical for scaling of sigma and g0 (see Details)
逻辑缩放西格玛和G0（见详情）
参数：...
other arguments
其他参数
Details
详细信息----------Details----------
The variables in the various linear predictors are described in secr models and listed for the particular model in the vars component of object.
秘书服务模型中描述的各种线性预测中的变量，并列出了在vars分量object的特定模型。
Optional newdata should be a dataframe with a column for each of the variables in the model (see "vars" component of object). If newdata is missing then a dataframe is constructed automatically. Default newdata are for a naive animal on the first occasion; numeric covariates are set to zero and factor covariates to their base (first) level.
可选newdata应该是一个数据框的变量在模型中的一列（请参阅“瓦尔object）”组件。如果newdata丢失，则自动构建一个数据框。默认newdata是一个天真的动物的第一次，数字协变量设置为零和因子协变量到他们的碱基（第一）水平。
Standard errors are by the delta method (Lebreton et al. 1992). Confidence intervals are backtransformed from the link scale.
标准错误是由delta方法（勒布雷顿等人，1992）。置信区间的逆转换的链接的规模。
The argument scaled applies only to the detection parameters g0 and sigma, and only to models fitted with scalesigma or scaleg0 switched on. If scaled is TRUE then each estimate is multiplied by its scale factor (1/D^0.5 and 1/sigma^2 respectively).
参数scaled仅适用于检测参数g0和标准差，而且只配备了scalesigma或scaleg0打开的模型。如果scaled是TRUE，则每个估计值是其比例因子乘以（1 / D ^ 0.5和1/sigma ^ 2）。
The value of newdata is optionally saved as an attribute.
newdata选择保存为一个属性。
detectpar is used to extract the detection parameter estimates from a simple model to pass to functions such as esa.plot. detectpar calls predict.secr. Parameters will be evaluated by default at base levels of the covariates, although this may be overcome by passing a one-line newdata to predict via the ... argument. Groups and mixtures are a headache for detectpar: it merely returns the estimated detection parameters of the first group or mixture.
detectpar使用，提取检测从一个简单的模型的参数估计值传递给函数如esa.plot。 detectparpredict.secr。将参数进行评估，默认情况下，在基本水平的协变量，虽然这可能是通过一newdata到predict通过克服...的说法。组和混合物，是一个头痛的detectpar：它只是返回的第一组或混合物的检测参数估计。
值----------Value----------
When se.fit = FALSE, a dataframe identical to newdata except for the addition of one column for each "real" parameter. Otherwise, a list with one component for each row in newdata. Each component is a dataframe with one row for each "real" parameter (density, g0, sigma, b) and columns as below
当se.fit= FALSEnewdata除了增加了一列，每一个真正的参数相同，一个数据框。否则，列表中的每一行newdata的一个组成部分。每个组件是一个带有一个行的每个真实的参数（密度，为g0，σ，二）和列如下所述，数据框
When newdata has only one row, the structure of the list is "dissolved" and the return value is one data frame.
当newdata只有一行，列表的结构的被“溶解”，返回值是一个数据框。
For detectpar, a list with the estimated values of detection parameters (e.g., g0 and sigma if detectfn = "halfnormal"). In the case of multi-session data the result is a list of lists (one list per session).
对于detectpar，检测参数（如，G0和sigma如果detectfn =“halfnormal”的）的估计值的列表。在多会话数据的情况下，其结果是一个列表的列表（每节一个列表）。
注意----------Note----------
predictDsurface should be used for predicting density at many points from a model with spatial variation. This deals automatically with scaling of x- and y-coordinates, and is much is faster than predict.secr. The resulting Dsurface object has its own plot method.
predictDsurface应被用于在许多点从一个空间变化模型预测密度。这优惠自动缩放的x坐标和y坐标，并是多少是速度比predict.secr。 Dsurface对象有其自己的土地法。
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
## load previously fitted secr model with trap response[＃加载预先安装SECR模型与陷阱响应]
## and extract estimates of `real' parameters for both[＃提取的真正的参数的估计为]
## naive (b = 0) and previously captured (b = 1) animals[＃天真的（b = 0时）和先前捕获的（b = 1的）的动物]
predict (secrdemo.b, newdata = data.frame(b=0:1))
temp <- predict (secrdemo.b, newdata = data.frame(b=0:1),
save = TRUE)
attr(temp, "newdata")
detectpar(secrdemo.0)
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 2012-9-30 00:01:44
