我正在尝试创建前两个主要成分的分数图。我首先根据 class 将数据分成三个数据框。然后我转换数据并执行 PCA。
我的数据如下:
14 1 82.0 12.80 7.60 1070 105 400
14 1 82.0 11.00 9.00 830 145 402
14 1 223.6 17.90 10.35 2200 135 500
15 1 164.0 14.50 9.80 1946 138 500
15 1 119.0 12.90 7.90 1190 140 400
15 1 74.5 7.50 6.30 653 177 350
15 1 74.5 11.13 8.28 930 113 402
16 1 279.5 14.30 9.40 1575 230 700
16 1 82.0 7.80 6.70 676 175 525
16 1 67.0 11.00 8.30 920 106 300
16 2 112.0 11.70 8.00 1353 140 560
16 2 149.0 12.80 8.70 1550 170 550
16 2 119.0 8.50 7.40 888 175 250
16 2 119.0 13.30 9.60 1275 157 450
16 2 238.5 14.90 8.90 1537 183 700
16 2 205.0 12.00 7.90 1292 201 600
16 2 82.0 9.40 6.20 611 209 175
16 2 119.0 15.95 10.25 1350 145 450
16 2 194.0 16.74 10.77 1700 120 450
17 2 336.0 22.20 10.90 3312 135 450
17 3 558.9 23.40 12.60 4920 152 600
17 3 287.0 14.30 9.40 1510 176 800
17 3 388.0 23.72 11.86 3625 140 500
17 3 164.0 11.90 9.80 900 190 600
17 3 194.0 14.40 9.20 1665 175 600
17 3 194.0 14.40 8.90 1640 175 600
17 3 186.3 9.70 8.00 1081 205 600
17 3 119.0 8.00 6.50 625 196 400
17 3 119.0 9.40 6.95 932 165 250
17 3 89.4 14.55 9.83 1378 146 400
第 1 列:type，第 2 列:class，第 3 列:v1，第 4 列:v2 , 第 5 列:v3，第 6 列:v4，第 7 列:v5，第 8 列:v6
我的代码如下:
data <- read.csv("data.csv")
result <- split(data, data$class);
data1 <- result[[1]][,3:8];
data1Logged <- log10(data1)
pca.data1Logged = prcomp( ~ v1 +
v2 +
v3 +
v4 +
v5 +
v6,
data = data1Logged, scale. = FALSE );
data2 <- result[[2]][,3:8];
data2Logged <- log10(data2)
pca.data2Logged = prcomp( ~ v1 +
v2 +
v3 +
v4 +
v5 +
v6,
data = data2Logged, scale. = FALSE );
data3 <- result[[3]][,3:8];
data3Logged <- log10(data3)
pca.data3Logged = prcomp( ~ v1 +
v2 +
v3 +
v4 +
v5 +
v6,
data = data3Logged, scale. = FALSE );
对于三个 class 中的每一个，我都想要 PC1 和 PC2 的分数图:
pca.data1Logged$x[,1:2]
pca.data2Logged$x[,1:2]
pca.data3Logged$x[,1:2]
这是我能想到的最好的:
opar <- par(mfrow = c(1,3))
plot(pca.data1Logged$x[,1:2])
plot(pca.data2Logged$x[,1:2])
plot(pca.data3Logged$x[,1:2])
par(opar)
但我希望对这个图进行缩放、着色、叠加等操作。我已经开始阅读有关 ggplot 的内容，但我没有这样做的经验。我想要如下内容:
https://cran.r-project.org/web/packages/ggfortify/vignettes/plot_pca.html
上面的问题是我将数据分成了 3 个单独的数据框，因此没有“class1”、“class2”、“class3”的标题。
最佳答案
您可以使用 factoextra 和 FactoMineR 之类的
library("factoextra")
library("FactoMineR")
#PCA analysis
df.pca <- PCA(df[,-c(1,2)], graph = T)
# Visualize
# Use habillage to specify groups for coloring
fviz_pca_ind(df.pca,
label = "none", # hide individual labels
habillage = as.factor(df$class), # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE # Concentration ellipses, legend.title = "Class")
您可以手动将 Dim1 和 2 更改为 PC1 和 2。为此，您可以从此图中记下“Dim1 (63.9%)”和“Dim2 (23.3%)”的值，并使用以下代码将 Dim1 和 2 更改为 PC1 和 2，如
fviz_pca_ind(df.pca,
label = "none", # hide individual labels
habillage = as.factor(df$class), # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
xlab = "PC1 (63.9%)", ylab = "PC2 (23.3%)", legend.title = "Class")
如果你想对数据进行日志转换，那么你可以使用
df[,3:8] <- log10(df[,3:8])
df.pca <- PCA(df, graph = T)
fviz_pca_ind(df.pca,
label = "none", # hide individual labels
habillage = as.factor(df$class), # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Class")
要手动将 Dim1 和 2 更改为 PC1 和 2，可以使用以下代码
fviz_pca_ind(df.pca,
label = "none", # hide individual labels
habillage = as.factor(df$class), # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
xlab = "PC1 (64.9%)", ylab = "PC2 (22.6%)", legend.title = "Class")
数据
df =
structure(list(Type = c(14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L), class = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), v1 = c(82, 82,
223.6, 164, 119, 74.5, 74.5, 279.5, 82, 67, 112, 149, 119, 119,
238.5, 205, 82, 119, 194, 336, 558.9, 287, 388, 164, 194, 194,
186.3, 119, 119, 89.4), v2 = c(12.8, 11, 17.9, 14.5, 12.9, 7.5,
11.13, 14.3, 7.8, 11, 11.7, 12.8, 8.5, 13.3, 14.9, 12, 9.4, 15.95,
16.74, 22.2, 23.4, 14.3, 23.72, 11.9, 14.4, 14.4, 9.7, 8, 9.4,
14.55), v3 = c(7.6, 9, 10.35, 9.8, 7.9, 6.3, 8.28, 9.4, 6.7,
8.3, 8, 8.7, 7.4, 9.6, 8.9, 7.9, 6.2, 10.25, 10.77, 10.9, 12.6,
9.4, 11.86, 9.8, 9.2, 8.9, 8, 6.5, 6.95, 9.83), v4 = c(1070L,
830L, 2200L, 1946L, 1190L, 653L, 930L, 1575L, 676L, 920L, 1353L,
1550L, 888L, 1275L, 1537L, 1292L, 611L, 1350L, 1700L, 3312L,
4920L, 1510L, 3625L, 900L, 1665L, 1640L, 1081L, 625L, 932L, 1378L
), v5 = c(105L, 145L, 135L, 138L, 140L, 177L, 113L, 230L, 175L,
106L, 140L, 170L, 175L, 157L, 183L, 201L, 209L, 145L, 120L, 135L,
152L, 176L, 140L, 190L, 175L, 175L, 205L, 196L, 165L, 146L),
v6 = c(400L, 402L, 500L, 500L, 400L, 350L, 402L, 700L, 525L,
300L, 560L, 550L, 250L, 450L, 700L, 600L, 175L, 450L, 450L,
450L, 600L, 800L, 500L, 600L, 600L, 600L, 600L, 400L, 250L,
400L)), class = "data.frame", row.names = c(NA, -30L))
关于r - 使用主成分构建分数图，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/63462344/