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Testing i.pca ~ prcomp(), m.eigensystem ~ princomp() 1. Briefly about PCA 2. The modules/functions that implement PCA in GRASS & R 3. My claims (Entitled Comments) 4. Evidence (=the numbers derived from i.pca, prcomp, princomp, m.eigensystem using some MODIS surface reflectance bands). Finally all is clear _but_ one thing: the only “unknown” variable (to […] ############################### PCA ############################### install.packages(“vegan”) library(vegan) > STpcoa<-read.table(file=”bactera_16s_final.subsample.phylip.tre1.weighted.phylip.pcoa.axes”, header=T,row.names=1) > STpcoa axis1 axis2 axis3 axis4 Cellulose -0.020878 -0.234601 0.167454 0 Foodwaste -0.234592 0.221741 0.085802 0 Sludge 0.368882 0.100725 -0.010570 0 Xylan -0.113413 -0.087865 -0.242686 0 >pl.STpcoa<-princomp(STpcoa) > summary(pl.STpcoa) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 0.2260563 0.1746944 0.1536319 0 Proportion of Variance 0.4856521 0.2900347 […] Difference between prcomp and princomp: ‘princomp’ can only be used with more units than variables” prcomp是基于SVD分解（svd()函数，princomp是基于特征向量eigen()函数) Good video source: http://www.youtube.com/watch?v=oZ2nfIPdvjY http://www.youtube.com/watch?v=I5GxNzKLIoU&feature=relmfu http://www.planta.cn/forum/viewtopic.php?t=16754&highlight=%D3%EF%D1%D4 ########################################### 以下所有代码包括练习数据，都可在R平台上直接运行。 #主成分分析和主成分回归 主成分分析的思想是Pearson 1901年提出的，Hotelling 1933进一步发展 在R中，进行主成分分析用到princomp() 函数 用法 princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), …) # 分析用数据 # cor 是否用样本的协方差矩阵作主成分分析 prcomp() 二 summary()函数 三 […] |
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