modSel(unmarked)
modSel()所属R语言包：unmarked
Model selection results from an unmarkedFitList
模型选择结果从unmarkedFitList
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Model selection results from an unmarkedFitList
模型选择结果从unmarkedFitList
参数----------Arguments----------
参数：object
an object of class "unmarkedFitList" created by the function fitList.
对象类“unmarkedFitList”的功能fitList。
参数：nullmod
optional character naming which model in the fitList contains results from the null model. Only used in calculation of Nagelkerke's R-squared index.
可选的字符命名哪个模型在fitList包含空模型的结果。仅用于Nagelkerke R-平方指数的计算。
值----------Value----------
A S4 object with the following slots
以下插槽一个S4对象
参数：Full
data.frame with formula, estimates, standard errors and model selection information. Converge is optim convergence code. CondNum is model condition number. n is the number of sites. delta is delta AIC. cumltvWt is cumulative AIC weight. Rsq is Nagelkerke's (1991) R-squared index, which is only returned when the nullmod argument is specified.
数据框公式，估计标准误差和模型选择信息。收敛的的OPTIM收敛代码。 CondNum是模型条件数。 n是网站的数量。Delta是DeltaAIC。 cumltvWt是累积性的AIC重量。 RSQ是Nagelkerke（1991）的R-平方的指数，这是唯一的nullmod指定参数时返回。
参数：Names
matrix referencing column names of estimates (row 1) and standard errors (row 2).
矩阵引用的列名的估计（第1行）和标准错误（第2行）。
注意----------Note----------
Two requirements exist to conduct AIC-based model-selection and model-averaging in unmarked. First, the data objects (ie, unmarkedFrames) must be identical among fitted models. Second, the response matrix must be identical among fitted models after missing values have been removed. This means that if a response value was removed in one model due to missingness, it needs to be removed from all models.
两个条件的存在是为了进行AIC模型选择和模型平均在无人盯防的。首先，数据对象（即，unmarkedFrames）拟合模型之间必须是相同的。其次，响应矩阵必须是相同的拟合模型之间后缺失值已被删除。这意味着，如果响应值除去在一个模型由于missingness，它需要被从所有模型中移除。
（作者）----------Author(s)----------
Richard Chandler <a href="mailto:rchandler@usgs.gov">rchandler@usgs.gov</a>
参考文献----------References----------
实例----------Examples----------
data(linetran)
(dbreaksLine <- c(0, 5, 10, 15, 20))
lengths <- linetran$Length * 1000
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat), dist.breaks = dbreaksLine,
tlength = lengths, survey = "line", unitsIn = "m")
})
fm1 <- distsamp(~ 1 ~1, ltUMF)
fm2 <- distsamp(~ area ~1, ltUMF)
fm3 <- distsamp( ~ 1 ~area, ltUMF)
fl <- fitList(Null=fm1, A.=fm2, .A=fm3)
fl
ms <- modSel(fl, nullmod="Null")
ms
coef(ms) # Estimates only[估计仅]
SE(ms) # Standard errors only[标准误差仅]
(toExport <- as(ms, "data.frame")) # Everything[一切]
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。
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发表于 2012-10-1 13:25:01
