lda(MASS)
lda()所属R语言包：MASS
Linear Discriminant Analysis
线性判别分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Linear discriminant analysis.
线性判别分析。
用法----------Usage----------
lda(x, ...)
## S3 method for class 'formula'[类formula的方法]
lda(formula, data, ..., subset, na.action)
## Default S3 method:[默认方法]
lda(x, grouping, prior = proportions, tol = 1.0e-4,
method, CV = FALSE, nu, ...)
## S3 method for class 'data.frame'
lda(x, ...)
## S3 method for class 'matrix'
lda(x, grouping, ..., subset, na.action)
参数----------Arguments----------
参数：formula
A formula of the form groups ~ x1 + x2 + ... That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.
一个公式的形式groups ~ x1 + x2 + ...也就是说，响应分组因素，并在右侧指定鉴（非因子）。
参数：data
Data frame from which variables specified in formula are preferentially to be taken.
数据框中指定的变量formula是优先要采取的。
参数：x
(required if no formula is given as the principal argument.) a matrix or data frame or Matrix containing the explanatory variables.
（如果没有公式作为主要参数。）矩阵或数据框或矩阵包含的解释变量。
参数：grouping
(required if no formula principal argument is given.) a factor specifying the class for each observation.
（如果没有公式的主要参数要求。）指定观察每个类的一个因素。
参数：prior
the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.
类成员的先验概率。如果未指定，训练集类比例。如果目前的概率应指定因子水平的顺序。
参数：tol
A tolerance to decide if a matrix is singular; it will reject variables and linear combinations of unit-variance variables whose variance is less than tol^2.
公差，以决定是否矩阵是奇异的，它会拒绝变量的线性组合和单位方差变量的方差比tol^2。
参数：subset
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
索引向量指定要在训练样本的情况下。 （注：如果给定的，这个参数必须命名）
参数：na.action
A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)
如果NA的发现将采取的一个函数来指定动作。默认操作是失败的过程。另一种方法是的na.omit，从而导致拒绝与任何所需的变量的缺失值的情况下。 （注：如果给定的，这个参数必须命名）
参数：method
"moment" for standard estimators of the mean and variance, "mle" for MLEs, "mve" to use cov.mve, or "t" for robust estimates based on a t distribution.
"moment"标准的均值和方差估计，"mle"极大似然估计，"mve"使用cov.mve或"t"基于鲁棒估计<X >分布。
参数：CV
If true, returns results (classes and posterior probabilities) for leave-one-out cross-validation. Note that if the prior is estimated, the proportions in the whole dataset are used.
如果为true，返回留一出交叉验证的结果（类和后验概率）。请注意，如果事先估计，在整个数据集的比例。
参数：nu
degrees of freedom for method = "t".
度method = "t"自由的。
参数：...
arguments passed to or from other methods. </table>
参数传递或其他方法。 </ TABLE>
Details
详情----------Details----------
The function tries hard to detect if the within-class covariance matrix is singular. If any variable has within-group variance less than tol^2 it will stop and report the variable as constant. This could result from poor scaling of the problem, but is more likely to result from constant variables.
函数试图防不胜防，如果类内协方差矩阵是奇异的。如果任何变量组内方差比tol^2“它将停止和报告为常数的变量。这可能会导致从贫困缩放的问题，但更可能导致常数变量。
Specifying the prior will affect the classification unless over-ridden in predict.lda. Unlike in most statistical packages, it will also affect the rotation of the linear discriminants within their space, as a weighted between-groups covariance matrix is used. Thus the first few linear discriminants emphasize the differences between groups with the weights given by the prior, which may differ from their prevalence in the dataset.
指定会影响prior在predict.lda除非覆盖分类。不像大多数统计软件包，它也将影响其空间内的线性判别的旋转，组间加权协方差矩阵。因此，最初的几个线性判别强调的重量之前，可能不同于他们的DataSet中的患病率与群体之间的差异。
If one or more groups is missing in the supplied data, they are dropped with a warning, but the classifications produced are with respect to the original set of levels.
如果一个或多个组提供的数据丢失，他们都将被丢弃警告，但产生的分类到原来的水平。
值----------Value----------
If CV = TRUE the return value is a list with components class, the MAP classification (a factor), and posterior, posterior probabilities for the classes.
如果CV = TRUE返回值是一个组件列表class，地图分类（因素），posterior类的后验概率。
Otherwise it is an object of class "lda" containing the following components:
否则，它是一个对象类"lda"包含以下组件：
参数：prior
the prior probabilities used.
先验概率。
参数：means
the group means.
组的意思。
参数：scaling
a matrix which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical.
矩阵转换观测判别函数，规范化，使组内协方差矩阵是球形。
参数：svd
the singular values, which give the ratio of the between- and within-group standard deviations on the linear discriminant variables. Their squares are the canonical F-statistics.
奇异值，从而使该线性判别变量间和组内标准偏差的比例。其广场是典型的F-统计。
参数：N
The number of observations used.
用于观测的数量。
参数：call
The (matched) function call.
（匹配）函数调用。
注意----------Note----------
This function may be called giving either a formula and optional data frame, or a matrix and grouping factor as the first two arguments. All other arguments are optional, but subset= and na.action=, if required, must be fully named.
此功能可以被称为一个公式和可选的数据框，或矩阵和分组因素，前两个参数。所有其他参数都是可选的，但subset=和na.action=，如果需要的话，必须充分名为。
If a formula is given as the principal argument the object may be modified using update() in the usual way.
如果一个公式作为主要参数的对象可以使用update()通常的方式进行修改。
参考文献----------References----------
Modern Applied Statistics with S. Fourth edition. Springer.
Pattern Recognition and Neural Networks. Cambridge University Press.
参见----------See Also----------
predict.lda, qda, predict.qda
predict.lda，qda，predict.qda
举例----------Examples----------
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
train <- sample(1:150, 75)
table(Iris$Sp[train])
## your answer may differ[＃你的答案可能会有所不同]
## c s v[＃C的V]
## 22 23 30[＃22 23 30]
z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)
predict(z, Iris[-train, ])$class
## [1] s s s s s s s s s s s s s s s s s s s s s s s s s s s c c c[＃[1] sssssssssssssssssssss ssssssccc]
## [31] c c c c c c c v c c c c v c c c c c c c c c c c c v v v v v[＃[31] cccccccvccccvcccccccc ccccvvvvv]
## [61] v v v v v v v v v v v v v v v[＃[61] V V V V V V V V V V V V V V V]
(z1 <- update(z, . ~ . - Petal.W.))
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 2012-2-16 19:57:47
