diagDA(sfsmisc)
diagDA()所属R语言包：sfsmisc
Diagonal Discriminant Analysis
对角线判别分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function implements a simple Gaussian maximum likelihood discriminant rule, for diagonal class covariance matrices.
此功能实现了一个简单的高斯最大似然判别规则，协方差矩阵为对角类。
用法----------Usage----------
dDA(x, cll, pool = TRUE)
## S3 method for class 'dDA'
predict(object, newdata, pool = object$pool, ...)
## S3 method for class 'dDA'
print(x, ...)
diagDA(ls, cll, ts, pool = TRUE)
参数----------Arguments----------
参数：x,ls
learning set data matrix, with rows corresponding to cases (e.g., mRNA samples) and columns to predictor variables (e.g., genes).
学习集数据矩阵，行对应的情况下（例如，mRNA样品）和列预测变量（例如，基因）。
参数：cll
class labels for learning set, must be consecutive integers.
学习组类的标签，必须是连续的整数。
参数：object
object of class dDA.
对象类dDA。
参数：ts, newdata
test set (prediction) data matrix, with rows corresponding to cases and columns to predictor variables.
测试集（预测）的数据矩阵的行对应的情况和列预测变量。
参数：pool
logical flag. If true (by default), the covariance matrices are assumed to be constant across classes and the discriminant rule is linear in the data. Otherwise (pool= FALSE), the covariance matrices may vary across classes and the discriminant rule is quadratic in the data.
逻辑标志。如果为true（默认情况下），协方差矩阵被认为是恒定的类，并判别是线性的数据。否则（pool= FALSE），协方差矩阵可能会有所不同跨类的判别规则是二次的数据的。
参数：...
further arguments passed to and from methods.
进一步传递的参数和方法。
值----------Value----------
dDA() returns an object of class dDA for which there are print and predict methods. The latter returns the same as diagDA():
dDA()返回一个类的对象dDA有print和predict方法。后者作为diagDA()返回相同的：
diagDA() returns an integer vector of class predictions for the test set.
diagDA()返回一个整数向量类，测试组的预测。
（作者）----------Author(s)----------
Sandrine Dudoit, <a href="mailto:sandrine@stat.berkeley.edu">sandrine@stat.berkeley.edu</a> and<br>
Jane Fridlyand, <a href="mailto:janef@stat.berkeley.edu">janef@stat.berkeley.edu</a> originally wrote
<code>stat.diag.da()</code> in CRAN package <span class="pkg">sma</span> which was modified
for speedup by Martin Maechler <a href="mailto:maechler@R-project.org">maechler@R-project.org</a>
who also introduced <code>dDA</code> etc.
参考文献----------References----------
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)
参见----------See Also----------
lda and qda from the MASS package.
lda和qdaMASS包。
实例----------Examples----------
## two artificial examples by Andreas Greutert:[两个人工由Andreas Greutert的例子：]
d1 <- data.frame(x = c(1, 5, 5, 5, 10, 25, 25, 25, 25, 29),
y = c(4, 1, 2, 4, 4, 4, 6:8, 7))
n.plot(d1)
library(cluster)
(cl1P <- pam(d1,k=4)$cluster) # 4 surprising clusters[4惊人的聚类]
with(d1, points(x+0.5, y, col = cl1P, pch =cl1P))
i1 <- c(1,3,5,6)
tr1 <- d1[-i1,]
cl1. <- c(1,2,1,2,1,3)
cl1 <- c(2,2,1,1,1,3)
plot(tr1, cex=2, col = cl1, pch = 20+cl1)
(dd.<- diagDA(tr1, cl1., ts = d1[ i1,]))# ok[行]
(dd <- diagDA(tr1, cl1 , ts = d1[ i1,]))# ok, too![好了，太！]
points(d1[ i1,], pch = 10, cex=3, col = dd)
## use new fit + predict instead :[＃使用新飞度+预测，而不是：]
(r1 <- dDA(tr1, cl1))
(r1.<- dDA(tr1, cl1.))
stopifnot(dd == predict(r1, new = d1[ i1,]),
dd.== predict(r1., new = d1[ i1,]))
plot(tr1, cex=2, col = cl1, bg = cl1, pch = 20+cl1,
xlim=c(1,30), ylim= c(0,10))
xy <- cbind(x= runif(500, min=1,max=30), y = runif(500, min=0, max=10))
points(xy, cex= 0.5, col = predict(r1, new = xy))
abline(v=c( mean(c(5,25)), mean(c(25,29))))
## example where one variable xj has Var(xj) = 0:[＃例如，其中一个变量XJ无功（XJ）= 0：]
x4 <- matrix(c(2:4,7, 6,8,5,6, 7,2,3,1, 7,7,7,7), ncol=4)
y <- c(2,2, 1,1)
m4.1 <- dDA(x4, y, pool = FALSE)
m4.2 <- dDA(x4, y, pool = TRUE)
xx <- matrix(c(3,7,5,7), ncol=4)
predict(m4.1, xx)## gave integer(0) previously[＃给以前整数（0）]
predict(m4.2, xx)
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 01:36:22
