taojiea1014的博客

第二章：Statistical Modeling目录2.2 The difference between statistical and probabilistic model2.3 A simple example of statistical modeling2.3.1 Classical statistics for classical data2.4 Binomial distributions and maximum likelihood2.4.1 An example2.5 More boxes

主成分分析可以简单的总结成一句话：数据的压缩和解释。常被用来寻找判断某种事物或现象的综合指标，并且给综合指标所包含的信息以适当的解释。在实际的应用过程中，主成分分析常被用作达到目的的中间手段，而非完全的一种分析方法。可以通过矩阵变换知道原始数据能够浓缩成几个主成分，以及每个主成分与原来变量之间线性组合关系式。但是细心的朋友会发现，每个原始变量在主成分中都占有一定的分量，这些分量（载荷）之间的大小分...

kmd <- read.table("c:\\temp\\kmeansdata.txt",header=T)attach(kmd)names(kmd)par(mfrow=c(2,2))plot(x,y,pch=16)plot(x,y,col=group,pch=16)model <- kmeans(data.frame(x,y),6)plot(x,y,col=model[[1]])mo...

, you know the identity of each individual (unlike cluster analysis) and you want to know how theexplanatory variables contribute to the correct classification of individuals. The method works by unco...

student.pr <- princomp(student, cor = TRUE) summary(student.pr, loadings=TRUE)predict(student.pr)screeplot(student.pr,type="lines")R<-matrix(0, nrow=16, ncol=16, dimnames=list(names, names))for ...

factor.analy1<-function(S, m){p<-nrow(S); diag_S<-diag(S); sum_rank<-sum(diag_S)rowname<-paste("X", 1:p, sep="")colname<-paste("Factor", 1:m, sep="")A<-matrix(0, nrow=

test<-scale(test)ca<-cancor(test[,1:3],test[,4:6])U<-as.matrix(test[, 1:3])%*% ca$xcoefV<-as.matrix(test[, 4:6])%*% ca$ycoefplot(U[,1], V[,1], xlab="U1", ylab="V1")plot(U[,3], V[,3], xlab=...