如何保存 PCA 摘要？答案

【问题标题】：How to save PCA summary?如何保存 PCA 摘要？
【发布时间】：2021-05-30 06:27:39
【问题描述】：

我已使用prcomp 函数对我的数据执行 PCA。我可以使用 write.csv 函数在 csv 中保存其他数据，例如中心、比例、分数、旋转，但我不知道如何保存 PCA 摘要。

我使用的数据

structure(list(Location = c("Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", 
"Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", 
"Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", 
"Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay", 
"Luoyuan Bay", "Luoyuan Bay", "Luoyuan Bay"), Serial = 32:52, 
    Station = c("NDDZ91", "NDDZ92", "NDDZ94", "NDDZ95", "NDDZ96", 
    "NDDZ97", "NDDZ98", "NDDZ99", "NDDZ103", "NDDZ105", "NDDZ106", 
    "NDDZ107", "NDDZ112", "NDDZ113", "NDDZ114", "NDDZ115", "NDDZ116", 
    "NDDZ117", "NDDZ119", "NDDZ122", "NDDZ123"), Longitude = c(119.6875, 
    119.6641667, 119.7163889, 119.7425, 119.69, 119.7719444, 
    119.6622222, 119.7544444, 119.6644444, 119.6875, 119.7369444, 
    119.7672222, 119.6583333, 119.6327778, 119.5991667, 119.6769444, 
    119.7019444, 119.7419444, 119.6216667, 119.6722222, 119.6408333
    ), Latitude = c(26.34944444, 26.35972222, 26.36, 26.36027778, 
    26.37583333, 26.38277778, 26.38638889, 26.39277778, 26.41055556, 
    26.41972222, 26.42833333, 26.42861111, 26.43361111, 26.44416667, 
    26.45, 26.45, 26.44805556, 26.44805556, 26.46638889, 26.47166667, 
    26.4775), Petrolium = c(20.26723856, 35.12860786, 38.00639611, 
    485.9158071, 6.801285888, 130.2613429, 26.41691724, 21.47363409, 
    75.24255785, 9.088017875, 17.83869051, 106.4365614, 367.7372615, 
    26.95847112, 42.01753212, 17.56582294, 72.00256098, 382.5705508, 
    150.0004825, 452.8679126, 61.51137016), Sulfide = c(15.16, 
    47.67, 30.23, 75.05, 10.51, 38.75, 21.64, 35.97, 122.53, 
    6.95, 58.52, 75.92, 92.23, 10.85, 72.1, 28.46, 81.19, 195.59, 
    42.54, 277.4, 81.67), PAH = c(0.0479, 0.0638, 0.1312, 0.1293, 
    0.0589, 0.1032, 0.2725, 0.0712, 0.1173, 0.0703, 0.2097, 0.1261, 
    0.0685, 0.0796, 0.1353, 0.0354, 0.0323, 0.034, 0.0269, 0.0952, 
    0.0269), OCP = c(0.01071845, 0, 0, 0, 0, 0.004247812, 0, 
    0, 0, 0.002020821, 0, 0.055799207, 0.003690873, 0.019063717, 
    0, 0.173323781, 0.010429405, 0, 0, 0.013486877, 0), PES = c(0, 
    44.15139004, 17.922209, 35.47228577, 0, 66.9289217, 11.62692114, 
    30.51552678, 57.2039725, 35.11363161, 49.43776022, 83.9088046, 
    0, 0, 45.56461801, 0, 0, 8.461490717, 29.69151766, 76.46222766, 
    34.05399385), Acrylic = c(0, 0, 0, 0, 0, 0, 0, 30.51552678, 
    0, 0, 0, 41.9544023, 0, 0, 0, 0, 0, 0, 0, 0, 0), PP = c(0, 
    0, 0, 0, 5.263920851, 0, 0, 0, 0, 0, 49.43776022, 0, 186.2662335, 
    248.4375, 0, 0, 0, 4.230745359, 0, 0, 0), Rayon = c(74.23500995, 
    58.86852005, 53.766627, 70.94457153, 26.31960425, 0, 69.76152682, 
    61.03105356, 114.407945, 70.22726323, 49.43776022, 41.9544023, 
    0, 0, 136.693854, 112.339267, 36.33232944, 16.92298143, 59.38303531, 
    305.8489106, 102.1619815)), class = "data.frame", row.names = c(NA, 
-21L))

我用于 PCA 的代码

Mydata = read.csv("PCA_Luoyuan.csv")
head(Mydata)
pca = prcomp(Mydata[,6:13], scale. = TRUE)
summary(pca)

【问题讨论】：

标签： r pca

【解决方案1】：

您可以从summary(pca) 中提取importance。

write.csv(summary(pca)$importance, 'temp.csv')

【讨论】：

【解决方案2】：

我们也可以使用write.table

write.table(summary(pca)$importance, "temp.txt")

【讨论】：