as.mlm.cca(vegan)
as.mlm.cca()所属R语言包：vegan
Refit Constrained Ordination as a Multiple Response Linear Model
作为一个多响应线性模型改装的约束排序
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Functions refit results of constrained ordination (cca, rda, capscale) as a multiple response linear model (lm). This allows finding influence statistics (influence.measures). This also allows deriving several other statistics, but most of these are biased and misleading, since refitting ignores a major component of variation in constrained ordination.
功能改装的约束协调的结果（cca，rda，capscale）作为一个多响应线性模型（lm）。这可以查找影响统计（influence.measures）。这也可以导出其他一些统计数据，但其中大部分都偏向和误导性的，因为在有限的协调，改装忽略的重要组成部分的变化。
用法----------Usage----------
as.mlm(x)
参数----------Arguments----------
参数：x
Constrained ordination result.
约束协调的结果。
Details
详细信息----------Details----------
Popular algorithm for constrained ordination is based on iteration with regression where weighted averages of sites are used as dependent variables and constraints as independent variables. Statistics of linear regression are a natural by-product in this algorithm. Constrained ordination in vegan uses different algorithm, but to obtain linear regression statistics you can refit an ordination result as a multiple response linear model (lm). This regression ignores residual unconstrained variation in the data, and therefore estimates of standard error are strongly biased and much too low. You can get statistics like t-values of coefficients, but you should not use these because of this bias. Some useful information you can get with refitted models are statistics for detecting influential observations (influence.measures including cooks.distance, hatvalues).
流行的算法迭代回归站点的加权平均数作为自变量，因变量和约束作为约束协调的基础上。在该算法中的副产物是一种自然的线性回归统计。 vegan的约束协调使用不同的算法，但获得的线性回归统计，你可以改装协调的结果多响应线性模型（lm）。此回归忽略剩余的无约束中的数据的变化，因此，标准误差的估计是强烈的偏见和过于低。你可以得到像t值系数的统计数据，但你不应该使用它们，因为这种偏见。改装的车型，你可以得到一些有用的信息是用于检测有影响力的意见（influence.measures包括cooks.distance，hatvalues的统计）。
值----------Value----------
Function returns an object of multiple response linear model of class "mlm" documented with lm.
函数返回一个对象的多响应线性模型的类"mlm"记录与lm的。
注意----------Note----------
You can use these functions to find t-values of coefficients using summary.mlm, but you should not do this because the method ignores unconstrained residual variation. You also can find several other statistics for (multiple response) linear models with similar bias. This bias is not a unique feature in vegan implementation, but also applies to implementations in other software.
您可以使用这些功能来找到t值使用summary.mlm系数，但你不应该这样做，因为该方法忽略了不受约束的残留变化。您还可以找到其他一些统计数据（可选多项）线性模型类似的偏见。这种偏见是不是一个独特的功能，在vegan实施，同时也适用于在其他软件中的实现。
Some statistics of linear models can be found without using these functions: coef.cca gives the regression coefficients, spenvcor the species-environment correlation, intersetcor the interset correlation, vif.cca the variance inflation factors.
一些统计数据的线性模型可以不使用这些功能：coef.cca给出了回归系数，spenvcor的物种环境相关，intersetcor：interset相关，vif.cca方差物价上涨的因素。
（作者）----------Author(s)----------
Jari Oksanen
参见----------See Also----------
cca, rda, capscale, cca.object, lm, summary.mlm,
cca，rda，capscale，cca.object，lm，summary.mlm，
实例----------Examples----------
data(varespec)
data(varechem)
mod <- cca(varespec ~ Al + P + K, data=varechem)
lmod <- as.mlm(mod)
## Coefficients[＃系数]
lmod
coef(mod)
## Influential observations[＃影响的观察]
influence.measures(lmod)
plot(mod, type = "n")
points(mod, cex = 10*hatvalues(lmod), pch=16, xpd = TRUE)
text(mod, display = "bp", col = "blue")
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 2012-10-1 15:03:01
