PCA分析：在dim desc()中得到错误：不方便数据。

Question

我对一组数据进行了PCA，使用的是 prcomp. 作为最后一步，我正在尝试使用。dimdesc() 功能从 FactoMineR 来获得p值，以确定与我的主成分相关最显著的变量。

数据框架有七个变量，所有这些变量都是数字，没有缺失值。名字都是标准的名字，比如 "RCH_Home"（以防名字可能有问题）。

我写了下面的函数。

res.desc <- dimdesc(df_PCA, axes = c(1:2), proba = 0.05)

然后得到以下错误信息Error in dimdesc(df_PCA, axes = c(1:2), proba = 0.05) : non convenient data

知道是怎么回事吗？

谢谢!!!

Answer 1

你应该使用 PCA 的功能。prcomp

下面是一个PCA的例子 FactoMineR.

library(FactoMineR)
library(factoextra)
library(paran)
data(cars)
mtcars_pca<-cars_pca<-PCA(mtcars)

如果你想检查方差的百分比，你可以这样做。

mtcars_pca$eig

> mtcars_pca$eig
        eigenvalue percentage of variance cumulative percentage of variance
comp 1  6.60840025             60.0763659                          60.07637
comp 2  2.65046789             24.0951627                          84.17153
comp 3  0.62719727              5.7017934                          89.87332
comp 4  0.26959744              2.4508858                          92.32421
comp 5  0.22345110              2.0313737                          94.35558
comp 6  0.21159612              1.9236011                          96.27918
comp 7  0.13526199              1.2296544                          97.50884
comp 8  0.12290143              1.1172858                          98.62612
comp 9  0.07704665              0.7004241                          99.32655
comp 10 0.05203544              0.4730495                          99.79960
comp 11 0.02204441              0.2004037                         100.00000

Cos2代表余弦的平方，是变量和个体质量的指数。这个值越接近于1，质量越好。

mtcars_pca$var$cos2
         Dim.1        Dim.2       Dim.3        Dim.4        Dim.5
mpg  0.8685312 0.0006891117 0.031962249 1.369725e-04 0.0023634487
cyl  0.9239416 0.0050717032 0.019276287 1.811054e-06 0.0007642822
disp 0.8958370 0.0064482423 0.002370993 1.775235e-02 0.0346868281
hp   0.7199031 0.1640467049 0.012295659 1.234773e-03 0.0651697911
drat 0.5717921 0.1999959326 0.016295731 1.970035e-01 0.0013361275
wt   0.7916038 0.0542284172 0.073281663 1.630161e-02 0.0012578888
qsec 0.2655437 0.5690984542 0.101947952 1.249426e-03 0.0060588455
vs   0.6208539 0.1422249798 0.115330572 1.244460e-02 0.0803189801
am   0.3647715 0.4887450097 0.026555457 2.501834e-04 0.0018011675
gear 0.2829342 0.5665806069 0.052667265 1.888829e-02 0.0005219259
carb 0.3026882 0.4533387304 0.175213444 4.333912e-03 0.0291718181

res.desc <- dimdesc(mtcars_pca, axes = c(1:2), proba = 0.05)



    > head(res.desc)
$Dim.1
$quanti
     correlation      p.value
cyl    0.9612188 2.471950e-18
disp   0.9464866 2.804047e-16
wt     0.8897212 9.780198e-12
hp     0.8484710 8.622043e-10
carb   0.5501711 1.105272e-03
qsec  -0.5153093 2.542578e-03
gear  -0.5319156 1.728737e-03
am    -0.6039632 2.520665e-04
drat  -0.7561693 5.575736e-07
vs    -0.7879428 8.658012e-08
mpg   -0.9319502 9.347042e-15

attr(,"class")
[1] "condes" "list " 

$Dim.2
$quanti
     correlation      p.value
gear   0.7527155 6.712704e-07
am     0.6991030 8.541542e-06
carb   0.6733043 2.411011e-05
drat   0.4472090 1.028069e-02
hp     0.4050268 2.147312e-02
vs    -0.3771273 3.335771e-02
qsec  -0.7543861 6.138696e-07

attr(,"class")
[1] "condes" "list " 

$call
$call$num.var
[1] 1

$call$proba
[1] 0.05

$call$weights
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$call$X
                            Dim.1  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           -0.6572132031 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       -0.6293955058 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          -2.7793970426 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      -0.3117707086 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout    1.9744889419 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             -0.0561375337 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360           3.0026742880 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           -2.0553287289 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            -2.2874083842 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            -0.5263812077 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           -0.5092054932 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE           2.2478104359 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL           2.0478227622 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC          2.1485421615 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood   3.8997903717 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental  3.9541231097 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial    3.5929719882 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            -3.8562837567 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         -4.2540325032 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      -4.2342207436 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       -1.9041678566 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger     2.1848507430 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin          1.8633834347 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28           2.8889945733 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird     2.2459189274 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           -3.5739682964 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       -2.6512550541 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        -3.3857059882 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L       1.3729574238 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        -0.0009899207 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora        2.6691258658 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          -2.4205931001 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

应该使用同一软件包中的函数。

如果你想选择你所需要的维度，你可以通过以下方法来实现 param 包裹

library(paran)
cars_paran<-paran(mtcars, graph = TRUE)