Specifying columns for PCA

[Mahmood Naderan]

Hi,

I use the following code and it works properly

> library(FactoMineR)
> mydata <- read.csv('test.csv', header=T,row.names=1)
> head(mydata)
     V1  V2   V3  V4
P1 73.6 0.7 74.6 3.1
P2 75.2 0.7 75.8 2.8
P3  6.5 0.0  7.3 2.5
P4 41.4 0.3 39.2 8.9
P5  5.4 0.1 18.2 1.1
P6 18.8 0.3 30.3 7.3

> pca.res<-PCA(mydata,graph=F,scale.unit=T)

>  plot(pca.res)

I would like to know how can I specify custom columns instead of all V1~V4?

For example, I want to use V1,V3,V4 and then I want to use V1,V2,V4 for further runs.

Regards,
Mahmood

[lepape...@neuf.fr]

Indicate the number of each column you want ?

# Without V3

> pca.res<-PCA(mydata [ , c (1,2,4)], graph=F, scale.unit=T)

Gilles

 

De : factomin...@googlegroups.com <factomin...@googlegroups.com> De la part de Mahmood Naderan
Envoyé : lundi 22 février 2021 18:23
À : factomin...@googlegroups.com
Objet : Specifying columns for PCA

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[Mahmood Naderan]

Thanks. It works for that data. However, when I change the structure, I am not able to use the idea

> library(FactoMineR)
> mydata <- read.csv('test.csv', header=T,row.names=1)

> mydata$CTG <- as.factor(mydata$CTG)
> head(mydata)
     V1  V2   V3  V4 CTG
P1 73.6 0.7 74.6 3.1   1
P2 75.2 0.7 75.8 2.8   1
P3  6.5 0.0  7.3 2.5   2
P4 41.4 0.3 39.2 8.9   2
P5  5.4 0.1 18.2 1.1   2
P6 18.8 0.3 30.3 7.3   3
> pca.res<-PCA(mydata[,c(1,2,4)],graph=T,scale.unit=T,quali.sup=5)
Error in `[.data.frame`(Xtot, , quali.sup, drop = FALSE) :
  undefined columns selected

With c(1,2,4) I expect V1,V2,V4 and the fifth column is supplementary.

Regards,
Mahmood

Cette discussion peut être lue sur le Web à l'adresse https://groups.google.com/d/msgid/factominer-users/017101d7094d%2408f98120%241aec8360%24%40neuf.fr.

[Mahmood Naderan]

Hi again,

Still the previous problems exist. When I use quali.sup, I am not able to specify which columns to be used in pca. May I know how to fix that?

> library(FactoMineR)

> mydata <- read.csv('test.csv', header=T,row.names=1)
> mydata

     V1  V2   V3  V4 CTG

P1 73.6 0.7 74.6 3.1  A1
P2 75.2 0.7 75.8 2.8  B1
P3  6.5 0.0  7.3 2.5  B1
P4 41.4 0.3 39.2 8.9  C1
P5  5.4 0.1 18.2 1.1  A1
P6 18.8 0.3 30.3 7.3  C1

> res.pca <- PCA(mydata[,c(1,2,4)])
> res.pca <- PCA(mydata[,c(1,2,4)],quali.sup=5)

Error in `[.data.frame`(Xtot, , quali.sup, drop = FALSE) :
  undefined columns selected

Regards,
Mahmood

[Mahmood Naderan]

Unfortunately that doesn't work either.

> mydata
     V1  V2   V3  V4 CTG
P1 73.6 0.7 74.6 3.1  A1
P2 75.2 0.7 75.8 2.8  B1
P3  6.5 0.0  7.3 2.5  B1
P4 41.4 0.3 39.2 8.9  C1
P5  5.4 0.1 18.2 1.1  A1
P6 18.8 0.3 30.3 7.3  C1

> res.pca <- PCA(mydata[,c(1,2,4,5)],quali.sup=5)
Error in PCA(mydata[, c(1, 2, 4, 5)], quali.sup = 5) :
The following variables are not quantitative:  CTG

Regards,
Mahmood

On Sun, Mar 7, 2021 at 1:23 PM J.C. Deroubaix <jc.der...@skynet.be> wrote:

Try res.pca <- PCA(mydata[,c(1,2,4,5)],quali.sup=5)

[J.C. Deroubaix]

Try

 res.pca <- PCA(mydata[,c(1,2,3,4,5)],quanti.sup=3,quali.sup=5)

or 

res.pca <- PCA(mydata,quanti.sup=3,quali.sup=5)

[Mahmood Naderan]

The commands you mentioned include the 3rd column in the PCA calculation.

My problem is that I want to exclude the 3rd column while using 1,2,4 as numerics and 5 as qualitative variables.

If there is no way to do that, I have to create another csv file without the 3rd column and do the rest of the thing. However, since that is a test file and maybe easy to do that, in my original dataset I have to create multiple csv file excluding different columns. However, if the PCA() has some facilities to include/exclude some quantitative and qualitative variables, it will be great.

Regards,
Mahmood

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