varpart(vegan)
varpart()所属R语言包：vegan
Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
社区矩阵分区的变化2，3，或4说明矩阵
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function partitions the variation of response table Y with respect to two, three, or four explanatory tables, using redundancy analysis ordination (RDA). If Y contains a single vector, partitioning is by partial regression. Collinear variables in the explanatory tables do NOT have to be removed prior to partitioning.
的功能分区的变化相对于两个，三个，或四个说明表，使用冗余分析协调（RDA）的响应表Y。如果Y包含一个向量，分区的部分回归。变量的解释表中不共线之前被删除分区。
用法----------Usage----------
varpart(Y, X, ..., data, transfo, scale = FALSE)
showvarparts(parts, labels, ...)
## S3 method for class 'varpart234'
plot(x, cutoff = 0, digits = 1, ...)
参数----------Arguments----------
参数：Y
Data frame or matrix containing the response data table. In community ecology, that table is often a site-by-species table.
数据框包含响应数据表或矩阵。在社会生态中，该表通常是一个网站种类表。
参数：X
Two to four explanatory models, variables or tables. These can be defined in three alternative ways: (1) one-sided model formulae beginning with ~ and then defining the model, (2) name of a single numeric variable, or (3) name of data frame or matrix with numeric variables. The model formulae can have factors, interaction terms and transformations of variables. The names of the variables in the model formula are found in data frame given in data argument, and if not found there, in the user environment. Single numeric variables, data frames or matrices are found in the user environment. All entries till the next argument (data or transfo) are interpreted as explanatory models, and the names of these arguments cannot be abbreviated nor omitted.
二至四个解释模型，变量或表。这些可以被定义在三种不同的方法：（1）片面的模型公式开始，~“，然后定义模型，（2）一个单一的数字变量名，或（3）名称的数据框或矩阵数值变量。该模型公式可以有因素的影响，相互作用和转换的变量。模型公式中的变量的名称被发现在data参数的数据框，如果没有找到，在用户环境中。单数值变量，数据框或矩阵被发现在用户环境中。所有参赛作品，直到下一个参数（data或transfo）被解释为解释模式，以及这些参数的名称不能缩写也没有省略。
参数：data
The data frame with the variables used in the formulae in X.
在公式中使用的变量在X的数据框。
参数：transfo
Transformation for Y (community data) using decostand. All alternatives in decostand can be used, and those preserving Euclidean metric include "hellinger", "chi.square", "total", "norm".
转型为Y（社区数据）使用decostand。所有的替代品decostand可以使用，和那些维护欧氏度量包括"hellinger"，"chi.square"，"total"，"norm"。
参数：scale
Should the columns of Y be standardized to unit variance
如果列Y标准化，以单位方差
参数：parts
Number of explanatory tables (circles) displayed.
显示的说明表中的号码（圆圈）。
参数：labels
Labels used for displayed fractions. Default is to use the same letters as in the printed output.
标签用于显示部分。默认是使用相同的字母在打印输出。
参数：x
The varpart result.
varpart结果。
参数：cutoff
The values below cutoff will not be displayed.
下面的cutoff的值将不被显示。
参数：digits
The number of significant digits; the number of decimal places is at least one higher.
的数量显著数字的小数位数至少有一个较高的。
参数：...
Other parameters passed to functions.
其他参数传递给函数。
Details
详细信息----------Details----------
The functions partition the variation in Y into components accounted for by two to four explanatory tables and their combined effects. If Y is a multicolumn data frame or matrix, the partitioning is based on redundancy analysis (RDA, see rda), and if Y is a single variable, the partitioning is based on linear regression. A simplified, fast version of RDA is used (function simpleRDA2). The actual calculations are done in functions varpart2 to varpart4, but these are not intended to be called directly by the user.
功能分区的变化Y到组件中占了两到四个的解释表和他们的联合作用。如果Y是多数据框或矩阵，分区是基于冗余分析（RDA，请参阅rda），而如果Y是一个单一的变量，分区是基于线性回归。 ，快速的简化版本RDA（函数simpleRDA2），。实际计算在功能varpart2varpart4，但这些不是由用户直接调用。
The function primarily uses adjusted R squares to assess the partitions explained by the explanatory tables and their combinations, because this is the only unbiased method (Peres-Neto et al., 2006). The raw R squares for basic fractions are also displayed, but these are biased estimates of variation explained by the explanatory table.
主要功能使用调整后R平方评估的说明表和它们的组合分区解释，因为这是唯一公正的方法（佩雷斯 - 内托等人，2006）。的原料ŕ正方形为基本的馏分也被显示，但这些被偏置估计的变化解释的说明表。
The identifiable fractions are designated by lower case alphabets. The meaning of the symbols can be found in the separate document "partitioning.pdf" (which can be read using vegandocs), or can be displayed graphically using function showvarparts.
可识别部分指定的小写字母。含义的符号，可以发现在单独的文件中的“partitioning.pdf”（这可以读取使用vegandocs），或可以以图形方式显示使用功能showvarparts。
A fraction is testable if it can be directly expressed as an RDA model. In these cases the printed output also displays the corresponding RDA model using notation where explanatory tables after | are conditions (partialled out; see rda for details). Although single fractions can be testable, this does not mean that all fractions simultaneously can be tested, since there number of testable fractions is higher than the number of estimated models.
一小部分是可测试的，如果它可以直接作为一个RDA模型表示。在这种情况下，打印输出也显示相应的RDA模型中使用的符号解释表后|是条件（partialled见rda）。虽然单一组分可以是可测试的，这并不意味着，所有组分可以同时进行测试，因为有可测试的馏分数高于估计模式的数目。
An abridged explanation of the alphabetic symbols for the individual fractions follows, but computational details should be checked in "partitioning.pdf" (readable with vegandocs) or in the source code.
的各个馏分的字母符号的删节解释如下，但应检查“partitioning.pdf”（可读与vegandocs）或在源代码中的计算的细节。
With two explanatory tables, the fractions explained uniquely by each of the two tables are [a] and [c], and their joint effect is [b] following Borcard et al. (1992).
有两种解释表中，唯一两个表中的每个部分解释是[a]和[c]，他们的共同作用是[b]以下Borcard等。 （1992）。
With three explanatory tables, the fractions explained uniquely by each of the three tables are [a] to [c], joint fractions between two tables are [d] to [f], and the joint fraction between all three tables is [g].
三个解释的表，唯一的三个表的分数解释是[a]到[c]，联合两个表之间的分数是[d]到[f]，并联合部分所有三个表之间是[g]。
With four explanatory tables, the fractions explained uniquely by each of the four tables are [a] to [d], joint fractions between two tables are [e] to [j], joint fractions between three variables are [k] to [n], and the joint fraction between all four tables is [o].
有四个解释的表，分数解释是[a]到[d]，联合两个表之间的分数是[e]到[j]，联合三部分之间唯一的四个表变量是[k]到[n]，并联合部分之间的所有四个表是[o]。
There is a plot function that displays the Venn diagram and labels each intersection (individual fraction) with the adjusted R squared if this is higher than cutoff. A helper function showvarpart displays the fraction labels.
有一个plot功能，显示的维恩图和标签的每个路口（个人部分）调整后的R平方这是高于cutoff，。一个辅助函数showvarpart显示部分标签。
值----------Value----------
Function varpart returns an object of class "varpart" with items scale and transfo (can be missing) which hold information on standardizations, tables which contains names of explanatory tables, and call with the function call. The function varpart calls function varpart2, varpart3 or varpart4 which return an object of class "varpart234" and saves its result in the item part. The items in this object are:
功能varpart返回一个类的对象"varpart"与项目scale和transfo（失踪），拥有标准化的信息，tables中包含的解释表，和call的功能call。函数varpart调用函数varpart2，varpart3或varpart4返回一个类的对象"varpart234"和保存其结果的项目part。这个对象中的项目有：
参数：SS.Y
Sum of squares of matrix Y.
平方矩阵Y总和。
参数：n
Number of observations (rows).
若干意见（行）。
参数：nsets
Number of explanatory tables
说明表数
参数：bigwarning
Warnings on collinearity.
警告共线性。
参数：fract
Basic fractions from all estimated constrained models.
基本分数从所有估计约束模型。
参数：indfract
Individual fractions or all possible subsections in the Venn diagram (see showvarparts).
个人的部分或所有可能的小节中的维恩图（见showvarparts“）。
参数：contr1
Fractions that can be found after conditioning on single explanatory table in models with three or four explanatory tables.
合并后可以发现，三个或四个解释表在单个表中型号说明与空调。
参数：contr2
Fractions that can be found after conditioning on two explanatory tables in models with four explanatory tables.
经过调理后两个解释模型中有四个解释的表的表，可以发现分数。
分数数据框----------Fraction Data Frames----------
Items fract, indfract, contr1 and contr2 are all data frames with items:
项目fract，indfract，contr1和contr2是与项目的所有数据框：
DfDegrees of freedom of numerator of the F-statistic for the fraction.
DfDegrees自由分子的F-统计量的比例。
R.squareRaw R-squared. This is calculated only for fract and this is NA in other items.
R.squareRaw R-平方。这是只计算fract，这是NA的其他项目。
Adj.R.squareAdjusted R-squared.
Adj.R.squareAdjusted R-平方。
TestableIf the fraction can be expressed as a (partial) RDA model, it is directly Testable, and this field is TRUE. In that case the fraction label also gives the specification of the testable RDA model.
TestableIf的馏分可以作为一个（部分）RDA模型来表示，它是直接Testable，并且这个字段是TRUE。在这种情况下的馏分标签还给出的可测试的RDA模型的规范。
注意----------Note----------
You can use command vegandocs to display document "partitioning.pdf" which presents Venn diagrams showing the fraction names in partitioning the variation of Y with respect to 2, 3, and 4 tables of explanatory variables, as well as the equations used in variation partitioning.
您可以使用命令vegandocs显示的文件“partitioning.pdf”维恩图的部分名称，在分区Y的变化与2，3，和4个表的解释变量，以及在方差分解公式。
The functions frequently give negative estimates of variation. Adjusted R-squares can be negative for any fraction; unadjusted R squares of testable fractions always will be non-negative. Non-testable fractions cannot be found directly, but by subtracting different models, and these subtraction results can be negative. The fractions are orthogonal, or linearly independent, but more complicated or nonlinear dependencies can cause negative non-testable fractions.
功能经常变化的负面估计。调整后的R平方是负面的任何部分，未经调整的ŕ广场可测试的分数始终将非负。非测试馏分不能直接找到，但减去不同的模型，和这些减法结果可以是负的。部分是正交的，线性无关，但更复杂的或非线性的依赖可能会导致负面的非测试部分。
The current function will only use RDA in multivariate partitioning. It is much more complicated to estimate the adjusted R-squares for CCA, and unbiased analysis of CCA is not currently implemented.
目前的功能将只使用RDA在多元的分区。估计调整后的R平方为CCA，和公正的分析CCA目前尚未实现，这是更复杂的。
（作者）----------Author(s)----------
Pierre Legendre, Departement de Sciences Biologiques, Universite de
Montreal, Canada. Adapted to <span class="pkg">vegan</span> by Jari Oksanen.
参考文献----------References----------
component of ecological variation. Ecology 73: 1045–1055.
Elsevier Science BV, Amsterdam.
transformations for ordination of species data. Oecologia 129: 271–280.
of species data matrices: estimation and comparison of fractions. Ecology 87: 2614–2625.
参见----------See Also----------
For analysing testable fractions, see rda and anova.cca. For data transformation, see decostand. Function inertcomp gives (unadjusted) components of variation for each species or site separately.
分析测试的分数，请参阅rda和anova.cca。数据转换，请参阅decostand。功能inertcomp（未经调整）成分的变化，每个物种或网站分开。
实例----------Examples----------
data(mite)
data(mite.env)
data(mite.pcnm)
## See detailed documentation:[＃查看详细的文档：]
## Not run: [＃不运行：]
vegandocs("partition")
## End(Not run)[＃（不执行）]
# Two explanatory matrices -- Hellinger-transform Y[两种解释矩阵 - 海灵格变换Y]
# Formula shortcut "~ ." means: use all variables in 'data'.[公式快捷键“~”。意思是：使用数据中的所有变量。]
mod <- varpart(mite, ~ ., mite.pcnm, data=mite.env, transfo="hel")
mod
showvarparts(2)
plot(mod)
# Alternative way of to conduct this partitioning[另一种方式进行分区]
# Change the data frame with factors into numeric model matrix[更改的数据框转换成数字模型矩阵的因素]
mm <- model.matrix(~ SubsDens + WatrCont + Substrate + Shrub + Topo, mite.env)[,-1]
mod <- varpart(decostand(mite, "hel"), mm, mite.pcnm)
# Test fraction [a] using partial RDA:[测试分数[A]使用局部RDA：]
aFrac <- rda(decostand(mite, "hel"), mm, mite.pcnm)
anova(aFrac, step=200, perm.max=200)
# RsquareAdj gives the same result as component [a] of varpart[RsquareAdj给出相同的结果，组分[a〕varpart]
RsquareAdj(aFrac)
# Three explanatory matrices [三个解释矩阵]
mod <- varpart(mite, ~ SubsDens + WatrCont, ~ Substrate + Shrub + Topo,
mite.pcnm, data=mite.env, transfo="hel")
mod
showvarparts(3)
plot(mod)
# An alternative formulation of the previous model using[以前的模型中使用的另一种表述]
# matrices mm1 amd mm2 and Hellinger transformed species data[矩阵MM1 AMD mm2和海灵格转化的物种数据]
mm1 <- model.matrix(~ SubsDens + WatrCont, mite.env)[,-1]
mm2 <- model.matrix(~ Substrate + Shrub + Topo, mite.env)[, -1]
mite.hel <- decostand(mite, "hel")
mod <- varpart(mite.hel, mm1, mm2, mite.pcnm)
# Use RDA to test fraction [a][使用RDA测试分数[A]]
# Matrix can be an argument in formula[可以是一个矩阵式的参数]
rda.result <- rda(mite.hel ~ mm1 + Condition(mm2) +
Condition(as.matrix(mite.pcnm)))
anova(rda.result, step=200, perm.max=200)
# Four explanatory tables[四解释的表]
mod <- varpart(mite, ~ SubsDens + WatrCont, ~Substrate + Shrub + Topo,
mite.pcnm[,1:11], mite.pcnm[,12:22], data=mite.env, transfo="hel")
mod
plot(mod)
# Show values for all partitions by putting 'cutoff' low enough:[把“截止”足够低，显示所有分区的值：]
plot(mod, cutoff = -Inf, cex = 0.7)
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 2012-10-1 15:14:39
