PCA(Table, quanti.sup =, graph = FALSE) 中的错误：以下变量不是定量的
Error in PCA(Table, quanti.sup =, graph = FALSE) : The following variables are not quantitative
我很想做 PCA，我用下面的命令来做这个：
res.table=PCA(table,quanti.sup=1:5,graph=FALSE)
但我收到以下错误：
Error in PCA(EAUX, quanti.sup = 1:5, graph = FALSE) :
The following variables are not quantitative: NOM
The following variables are not quantitative: ACRO
The following variables are not quantitative: PAYS
The following variables are not quantitative: TYPE
The following variables are not quantitative: PG
这是 table ：
dput(头(table)) :
structure(list(NOM = c("Evian", "Montagne des Pyrenees", "Cristaline-St-Cyr",
"Fiee des Lois", "Volcania", "Saint Diery"), ACRO = c("EVIAN",
"MTPYR", "CRIST", "FIEE", "VOLCA", "STDIE"), PAYS = c("F", "F",
"F", "F", "F", "F"), TYPE = c("M", "S", "S", "S", "S", "M"),
PG = c("P", "P", "P", "P", "P", "G"), CA = c(78, 48, 71,
89, 4.1, 85), MG = c(24, 11, 5.5, 31, 1.7, 80), `NA` = c(5,
34, 11.2, 17, 2.7, 385), K = c(1, 1, 3.2, 2, 0.9, 65), SUL = c(10,
16, 5, 47, 1.1, 25), NO3 = c(3.8, 4, 1, 0, 0.8, 1.9), HCO3 = c(357,
183, 250, 360, 25.8, 1350), CL = c(4.5, 50, 20, 28, 0.9,
285), MOY = c(60.41, 43.38, 45.86, 71.75, 4.75, 284.61)), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
谢谢
此代码应用于您发送的示例数据集：
library(fastDummies)
table <- data.frame(NOM = c("Evian", "Montagne des Pyrenees", "Cristaline-St-Cyr",
"Fiee des Lois", "Volcania", "Saint Diery"),
ACRO = c("EVIAN", "MTPYR", "CRIST", "FIEE", "VOLCA", "STDIE"),
PAYS = c("F", "F","F", "F", "F", "F"),
TYPE = c("M", "S", "S", "S", "S", "M"),
PG = c("P", "P", "P", "P", "P", "G"),
CA = c(78, 48, 71, 89, 4.1, 85),
MG = c(24, 11, 5.5, 31, 1.7, 80),
`NA` = c(5,34, 11.2, 17, 2.7, 385),
K = c(1, 1, 3.2, 2, 0.9, 65),
SUL = c(10,16, 5, 47, 1.1, 25),
NO3 = c(3.8, 4, 1, 0, 0.8, 1.9),
HCO3 = c(357,183, 250, 360, 25.8, 1350),
CL = c(4.5, 50, 20, 28, 0.9,285),
MOY = c(60.41, 43.38, 45.86, 71.75, 4.75, 284.61),
stringsAsFactors = TRUE)
table_conv <- dummy_cols(table,
select_columns = c("NOM","ACRO","PAYS","TYPE","PG"),
remove_selected_columns = TRUE)
pca <- prcomp(as.matrix(table_conv))
pca
首先，将分类变量转换为数字的一种简单方法是热编码。您可以使用库 fastDummies。
转换后，你可以使用R中的prcomp函数应用PCA。由于prcomp使用矩阵，你必须先转换它。
Standard deviations (1, .., p=6):
[1] 5.168512e+02 5.385374e+01 1.796098e+01 1.134412e+01 3.412325e+00 3.841269e-14
Rotation (n x k) = (26 x 6):
PC1 PC2 PC3 PC4
CA 3.406007e-02 0.4583258631 -0.449804772 0.583419015
MG 5.452648e-02 0.0567007399 -0.120337337 -0.431460022
NA. 2.857387e-01 -0.6581371706 -0.015607808 -0.028124352
K 4.881587e-02 -0.1004287315 0.078457844 -0.025483654
SUL 1.165818e-02 0.1358452663 -0.676545259 -0.582029743
NO3 6.980208e-05 -0.0009273267 0.013738946 0.056474181
HCO3 9.122393e-01 0.3046381925 0.184652450 -0.053887132
CL 2.046765e-01 -0.4804253636 -0.498672171 0.351834600
MOY 1.939708e-01 -0.0355753827 -0.185620957 -0.016150352
NOM_Cristaline-St-Cyr -1.441857e-04 0.0010557813 0.003263708 0.026324720
NOM_Evian -7.179722e-05 0.0044191573 0.013208312 -0.001548583
NOM_Fiee des Lois -6.100869e-05 0.0038456894 -0.014279448 -0.018364975
NOM_Montagne des Pyrenees -1.811771e-04 -0.0029622537 -0.012327470 0.013222722
NOM_Saint Diery 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
NOM_Volcania -3.100046e-04 -0.0046854729 0.008784342 -0.018588698
ACRO_CRIST -1.441857e-04 0.0010557813 0.003263708 0.026324720
ACRO_EVIAN -7.179722e-05 0.0044191573 0.013208312 -0.001548583
ACRO_FIEE -6.100869e-05 0.0038456894 -0.014279448 -0.018364975
ACRO_MTPYR -1.811771e-04 -0.0029622537 -0.012327470 0.013222722
ACRO_STDIE 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
ACRO_VOLCA -3.100046e-04 -0.0046854729 0.008784342 -0.018588698
PAYS_F 0.000000e+00 0.0000000000 0.000000000 0.000000000
TYPE_M 6.963761e-04 0.0027462560 0.014558868 -0.002593769
TYPE_S -6.963761e-04 -0.0027462560 -0.014558868 0.002593769
PG_G 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
PG_P -7.681733e-04 0.0016729014 -0.001350556 0.001045186
PC5 PC6
CA -0.117348632 0.135395809
MG 0.564075505 0.597488041
NA. -0.240227688 -0.095918767
K -0.452908211 0.420830725
SUL -0.283494587 -0.290154729
NO3 0.440026805 -0.480971856
HCO3 0.001376105 -0.077751617
CL 0.297037810 0.102927429
MOY 0.026458834 0.119000634
NOM_Cristaline-St-Cyr -0.074866123 0.150502225
NOM_Evian 0.066867097 -0.097960878
NOM_Fiee des Lois -0.034173834 0.021559689
NOM_Montagne des Pyrenees 0.072334607 -0.062871374
NOM_Saint Diery -0.004010064 0.001954674
NOM_Volcania -0.026151683 -0.009820343
ACRO_CRIST -0.074866123 0.150502225
ACRO_EVIAN 0.066867097 -0.097960878
ACRO_FIEE -0.034173834 0.021559689
ACRO_MTPYR 0.072334607 -0.062871374
ACRO_STDIE -0.004010064 0.001954674
ACRO_VOLCA -0.026151683 -0.009820343
PAYS_F 0.000000000 0.000000000
TYPE_M 0.062857033 -0.094621030
TYPE_S -0.062857033 0.097787141
PG_G -0.004010064 0.001954674
PG_P 0.004010064 -0.001822753
这里有虚拟转换和 PCA。相应地应用于您的数据。建议不要将列命名为 NA。这是一个保留名称。
我很想做 PCA，我用下面的命令来做这个：
res.table=PCA(table,quanti.sup=1:5,graph=FALSE)
但我收到以下错误：
Error in PCA(EAUX, quanti.sup = 1:5, graph = FALSE) :
The following variables are not quantitative: NOM
The following variables are not quantitative: ACRO
The following variables are not quantitative: PAYS
The following variables are not quantitative: TYPE
The following variables are not quantitative: PG
这是 table ：
dput(头(table)) :
structure(list(NOM = c("Evian", "Montagne des Pyrenees", "Cristaline-St-Cyr",
"Fiee des Lois", "Volcania", "Saint Diery"), ACRO = c("EVIAN",
"MTPYR", "CRIST", "FIEE", "VOLCA", "STDIE"), PAYS = c("F", "F",
"F", "F", "F", "F"), TYPE = c("M", "S", "S", "S", "S", "M"),
PG = c("P", "P", "P", "P", "P", "G"), CA = c(78, 48, 71,
89, 4.1, 85), MG = c(24, 11, 5.5, 31, 1.7, 80), `NA` = c(5,
34, 11.2, 17, 2.7, 385), K = c(1, 1, 3.2, 2, 0.9, 65), SUL = c(10,
16, 5, 47, 1.1, 25), NO3 = c(3.8, 4, 1, 0, 0.8, 1.9), HCO3 = c(357,
183, 250, 360, 25.8, 1350), CL = c(4.5, 50, 20, 28, 0.9,
285), MOY = c(60.41, 43.38, 45.86, 71.75, 4.75, 284.61)), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
谢谢
此代码应用于您发送的示例数据集：
library(fastDummies)
table <- data.frame(NOM = c("Evian", "Montagne des Pyrenees", "Cristaline-St-Cyr",
"Fiee des Lois", "Volcania", "Saint Diery"),
ACRO = c("EVIAN", "MTPYR", "CRIST", "FIEE", "VOLCA", "STDIE"),
PAYS = c("F", "F","F", "F", "F", "F"),
TYPE = c("M", "S", "S", "S", "S", "M"),
PG = c("P", "P", "P", "P", "P", "G"),
CA = c(78, 48, 71, 89, 4.1, 85),
MG = c(24, 11, 5.5, 31, 1.7, 80),
`NA` = c(5,34, 11.2, 17, 2.7, 385),
K = c(1, 1, 3.2, 2, 0.9, 65),
SUL = c(10,16, 5, 47, 1.1, 25),
NO3 = c(3.8, 4, 1, 0, 0.8, 1.9),
HCO3 = c(357,183, 250, 360, 25.8, 1350),
CL = c(4.5, 50, 20, 28, 0.9,285),
MOY = c(60.41, 43.38, 45.86, 71.75, 4.75, 284.61),
stringsAsFactors = TRUE)
table_conv <- dummy_cols(table,
select_columns = c("NOM","ACRO","PAYS","TYPE","PG"),
remove_selected_columns = TRUE)
pca <- prcomp(as.matrix(table_conv))
pca
首先，将分类变量转换为数字的一种简单方法是热编码。您可以使用库 fastDummies。
转换后，你可以使用R中的prcomp函数应用PCA。由于prcomp使用矩阵，你必须先转换它。
Standard deviations (1, .., p=6):
[1] 5.168512e+02 5.385374e+01 1.796098e+01 1.134412e+01 3.412325e+00 3.841269e-14
Rotation (n x k) = (26 x 6):
PC1 PC2 PC3 PC4
CA 3.406007e-02 0.4583258631 -0.449804772 0.583419015
MG 5.452648e-02 0.0567007399 -0.120337337 -0.431460022
NA. 2.857387e-01 -0.6581371706 -0.015607808 -0.028124352
K 4.881587e-02 -0.1004287315 0.078457844 -0.025483654
SUL 1.165818e-02 0.1358452663 -0.676545259 -0.582029743
NO3 6.980208e-05 -0.0009273267 0.013738946 0.056474181
HCO3 9.122393e-01 0.3046381925 0.184652450 -0.053887132
CL 2.046765e-01 -0.4804253636 -0.498672171 0.351834600
MOY 1.939708e-01 -0.0355753827 -0.185620957 -0.016150352
NOM_Cristaline-St-Cyr -1.441857e-04 0.0010557813 0.003263708 0.026324720
NOM_Evian -7.179722e-05 0.0044191573 0.013208312 -0.001548583
NOM_Fiee des Lois -6.100869e-05 0.0038456894 -0.014279448 -0.018364975
NOM_Montagne des Pyrenees -1.811771e-04 -0.0029622537 -0.012327470 0.013222722
NOM_Saint Diery 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
NOM_Volcania -3.100046e-04 -0.0046854729 0.008784342 -0.018588698
ACRO_CRIST -1.441857e-04 0.0010557813 0.003263708 0.026324720
ACRO_EVIAN -7.179722e-05 0.0044191573 0.013208312 -0.001548583
ACRO_FIEE -6.100869e-05 0.0038456894 -0.014279448 -0.018364975
ACRO_MTPYR -1.811771e-04 -0.0029622537 -0.012327470 0.013222722
ACRO_STDIE 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
ACRO_VOLCA -3.100046e-04 -0.0046854729 0.008784342 -0.018588698
PAYS_F 0.000000e+00 0.0000000000 0.000000000 0.000000000
TYPE_M 6.963761e-04 0.0027462560 0.014558868 -0.002593769
TYPE_S -6.963761e-04 -0.0027462560 -0.014558868 0.002593769
PG_G 7.681733e-04 -0.0016729014 0.001350556 -0.001045186
PG_P -7.681733e-04 0.0016729014 -0.001350556 0.001045186
PC5 PC6
CA -0.117348632 0.135395809
MG 0.564075505 0.597488041
NA. -0.240227688 -0.095918767
K -0.452908211 0.420830725
SUL -0.283494587 -0.290154729
NO3 0.440026805 -0.480971856
HCO3 0.001376105 -0.077751617
CL 0.297037810 0.102927429
MOY 0.026458834 0.119000634
NOM_Cristaline-St-Cyr -0.074866123 0.150502225
NOM_Evian 0.066867097 -0.097960878
NOM_Fiee des Lois -0.034173834 0.021559689
NOM_Montagne des Pyrenees 0.072334607 -0.062871374
NOM_Saint Diery -0.004010064 0.001954674
NOM_Volcania -0.026151683 -0.009820343
ACRO_CRIST -0.074866123 0.150502225
ACRO_EVIAN 0.066867097 -0.097960878
ACRO_FIEE -0.034173834 0.021559689
ACRO_MTPYR 0.072334607 -0.062871374
ACRO_STDIE -0.004010064 0.001954674
ACRO_VOLCA -0.026151683 -0.009820343
PAYS_F 0.000000000 0.000000000
TYPE_M 0.062857033 -0.094621030
TYPE_S -0.062857033 0.097787141
PG_G -0.004010064 0.001954674
PG_P 0.004010064 -0.001822753
这里有虚拟转换和 PCA。相应地应用于您的数据。建议不要将列命名为 NA。这是一个保留名称。