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How can I import and work with correlation matrix as the only data source in PCA and PCF in R
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Data science time! April 2019 and salary with experience
The Ask Question Wizard is Live!How can I create a correlation matrix in R?How to reconstruct the original data matrix after applying PCA in RHow can I view the source code for a function?PCA analysis using Correlation Matrix as input in RHow to retrieve eigenvalues & eigenvectors from Raster PCA in R?MATLAB perform PCA on the correlation matrixuse correlation matrix in robust PCA functions RHow can you create a correlation matrix in PCA on Python?PCA with zero and high correlation in dataDon't know why eigen() gives a vectors of wrong sign and the loading matrix is just vector
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I am new to R, and am working on a problem of *mporting and working with correlation matrix as the only data source in PCA and PCF in R
I have referred to stack overflow answer banks and even books, I could not find any hints, it make it like R only work with variables data file whereas in SAS you can simply input the correlation matrix and get the PCA and PCF result easily. Hope I am wrong.
I tried to look at stack overflow answer banks, and they are mostly about how to calculate the cor matrix or eigenvalue decomposition.
Below is my attempts:
setwd("D:/BlueHDD/MAQAB/RStudio/R/PCA/Intelligence")
mydata <- read.csv("Intelligence.csv",na.strings = ".")
head(mydata)
X M P C E H F
1 M 1.000 0.620 0.540 0.320 0.284 0.370
2 P 0.620 1.000 0.510 0.380 0.351 0.430
3 C 0.540 0.510 1.000 0.360 0.336 0.405
4 E 0.320 0.380 0.360 1.000 0.686 0.730
5 H 0.284 0.351 0.336 0.686 1.000 0.735
6 F 0.370 0.430 0.405 0.730 0.735 1.000
ii <- as.matrix(mydata[,2:7])
rownames(ii)<- c ("M","P","C","E","H","F")
colnames(ii)<- c ("M","P","C","E","H","F")
head(ii)
M P C E H F
M 1.000 0.620 0.540 0.320 0.284 0.370
P 0.620 1.000 0.510 0.380 0.351 0.430
C 0.540 0.510 1.000 0.360 0.336 0.405
E 0.320 0.380 0.360 1.000 0.686 0.730
H 0.284 0.351 0.336 0.686 1.000 0.735
F 0.370 0.430 0.405 0.730 0.735 1.000
myPCA <- eigen(ii)
head(myPCA)
$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
myPCA$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
myPCA$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
The problem now in the vector, all the "+" and "-" are opposite !
Also, from here, I don't know how to get the loading matrix. I tried the below but fails:
fit <- princomp(ii)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
r pca
edited Mar 10 at 16:18
halfer
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
add a comment |
I am new to R, and am working on a problem of *mporting and working with correlation matrix as the only data source in PCA and PCF in R
I have referred to stack overflow answer banks and even books, I could not find any hints, it make it like R only work with variables data file whereas in SAS you can simply input the correlation matrix and get the PCA and PCF result easily. Hope I am wrong.
I tried to look at stack overflow answer banks, and they are mostly about how to calculate the cor matrix or eigenvalue decomposition.
Below is my attempts:
setwd("D:/BlueHDD/MAQAB/RStudio/R/PCA/Intelligence")
mydata <- read.csv("Intelligence.csv",na.strings = ".")
head(mydata)
X M P C E H F
1 M 1.000 0.620 0.540 0.320 0.284 0.370
2 P 0.620 1.000 0.510 0.380 0.351 0.430
3 C 0.540 0.510 1.000 0.360 0.336 0.405
4 E 0.320 0.380 0.360 1.000 0.686 0.730
5 H 0.284 0.351 0.336 0.686 1.000 0.735
6 F 0.370 0.430 0.405 0.730 0.735 1.000
ii <- as.matrix(mydata[,2:7])
rownames(ii)<- c ("M","P","C","E","H","F")
colnames(ii)<- c ("M","P","C","E","H","F")
head(ii)
M P C E H F
M 1.000 0.620 0.540 0.320 0.284 0.370
P 0.620 1.000 0.510 0.380 0.351 0.430
C 0.540 0.510 1.000 0.360 0.336 0.405
E 0.320 0.380 0.360 1.000 0.686 0.730
H 0.284 0.351 0.336 0.686 1.000 0.735
F 0.370 0.430 0.405 0.730 0.735 1.000
myPCA <- eigen(ii)
head(myPCA)
$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
myPCA$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
myPCA$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
The problem now in the vector, all the "+" and "-" are opposite !
Also, from here, I don't know how to get the loading matrix. I tried the below but fails:
fit <- princomp(ii)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
r pca
edited Mar 10 at 16:18
halfer
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
Below is my attempts:
– Wilks
Mar 9 at 3:42
add a comment |
I am new to R, and am working on a problem of *mporting and working with correlation matrix as the only data source in PCA and PCF in R
I have referred to stack overflow answer banks and even books, I could not find any hints, it make it like R only work with variables data file whereas in SAS you can simply input the correlation matrix and get the PCA and PCF result easily. Hope I am wrong.
I tried to look at stack overflow answer banks, and they are mostly about how to calculate the cor matrix or eigenvalue decomposition.
Below is my attempts:
setwd("D:/BlueHDD/MAQAB/RStudio/R/PCA/Intelligence")
mydata <- read.csv("Intelligence.csv",na.strings = ".")
head(mydata)
X M P C E H F
1 M 1.000 0.620 0.540 0.320 0.284 0.370
2 P 0.620 1.000 0.510 0.380 0.351 0.430
3 C 0.540 0.510 1.000 0.360 0.336 0.405
4 E 0.320 0.380 0.360 1.000 0.686 0.730
5 H 0.284 0.351 0.336 0.686 1.000 0.735
6 F 0.370 0.430 0.405 0.730 0.735 1.000
ii <- as.matrix(mydata[,2:7])
rownames(ii)<- c ("M","P","C","E","H","F")
colnames(ii)<- c ("M","P","C","E","H","F")
head(ii)
M P C E H F
M 1.000 0.620 0.540 0.320 0.284 0.370
P 0.620 1.000 0.510 0.380 0.351 0.430
C 0.540 0.510 1.000 0.360 0.336 0.405
E 0.320 0.380 0.360 1.000 0.686 0.730
H 0.284 0.351 0.336 0.686 1.000 0.735
F 0.370 0.430 0.405 0.730 0.735 1.000
myPCA <- eigen(ii)
head(myPCA)
$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
myPCA$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
myPCA$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
The problem now in the vector, all the "+" and "-" are opposite !
Also, from here, I don't know how to get the loading matrix. I tried the below but fails:
fit <- princomp(ii)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
r pca
edited Mar 10 at 16:18
halfer
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
I am new to R, and am working on a problem of *mporting and working with correlation matrix as the only data source in PCA and PCF in R
I have referred to stack overflow answer banks and even books, I could not find any hints, it make it like R only work with variables data file whereas in SAS you can simply input the correlation matrix and get the PCA and PCF result easily. Hope I am wrong.
I tried to look at stack overflow answer banks, and they are mostly about how to calculate the cor matrix or eigenvalue decomposition.
Below is my attempts:
setwd("D:/BlueHDD/MAQAB/RStudio/R/PCA/Intelligence")
mydata <- read.csv("Intelligence.csv",na.strings = ".")
head(mydata)
X M P C E H F
1 M 1.000 0.620 0.540 0.320 0.284 0.370
2 P 0.620 1.000 0.510 0.380 0.351 0.430
3 C 0.540 0.510 1.000 0.360 0.336 0.405
4 E 0.320 0.380 0.360 1.000 0.686 0.730
5 H 0.284 0.351 0.336 0.686 1.000 0.735
6 F 0.370 0.430 0.405 0.730 0.735 1.000
ii <- as.matrix(mydata[,2:7])
rownames(ii)<- c ("M","P","C","E","H","F")
colnames(ii)<- c ("M","P","C","E","H","F")
head(ii)
M P C E H F
M 1.000 0.620 0.540 0.320 0.284 0.370
P 0.620 1.000 0.510 0.380 0.351 0.430
C 0.540 0.510 1.000 0.360 0.336 0.405
E 0.320 0.380 0.360 1.000 0.686 0.730
H 0.284 0.351 0.336 0.686 1.000 0.735
F 0.370 0.430 0.405 0.730 0.735 1.000
myPCA <- eigen(ii)
head(myPCA)
$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
myPCA$values
[1] 3.3670861 1.1941791 0.5070061 0.3718472 0.3131559 0.2467257
myPCA$vectors
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.3677678 -0.5098401 0.266985551 0.72768020 0.047584025 -0.04178482
[2,] -0.3913477 -0.4092063 0.485916591 -0.66464527 -0.005392018 -0.03872816
[3,] -0.3719504 -0.3825819 -0.831626240 -0.15204371 -0.003331423 -0.02352388
[4,] -0.4321872 0.3748248 0.021531885 0.06531777 -0.742970281 -0.34056682
[5,] -0.4219572 0.4214599 0.002730054 0.01174474 0.665109730 -0.44922966
[6,] -0.4565228 0.3288196 0.023032686 0.03473540 0.057617669 0.82365511
The problem now in the vector, all the "+" and "-" are opposite !
Also, from here, I don't know how to get the loading matrix. I tried the below but fails:
fit <- princomp(ii)
summary(fit) # print variance accounted for
loadings(fit) # pc loadings
plot(fit,type="lines") # scree plot
fit$scores # the principal components
biplot(fit)
r pca
r pca
edited Mar 10 at 16:18
halfer
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
edited Mar 10 at 16:18
halfer
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
edited Mar 10 at 16:18
halfer
14.8k759118
edited Mar 10 at 16:18
halfer
14.8k759118
edited Mar 10 at 16:18
halfer
14.8k759118
14.8k759118
asked Mar 9 at 2:09
WilksWilks
11
asked Mar 9 at 2:09
WilksWilks
11
asked Mar 9 at 2:09
WilksWilks
11
11
Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
Below is my attempts:
– Wilks
Mar 9 at 3:42
add a comment |
Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
Below is my attempts:
– Wilks
Mar 9 at 3:42
Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
Below is my attempts:
– Wilks
Mar 9 at 3:42
Below is my attempts:
– Wilks
Mar 9 at 3:42
add a comment |
1 Answer
1
active
oldest
votes
You can perform PCA in R with the princomp function. The documentation says that if you supply the argument covmat it will compute the principal components from the covariance matrix. But it also works to use this argument with the correlation matrix.
Here is a simple example using the iris data.
## principal components from the original data
princomp(iris[,1:4], cor=T)
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
Now suppose that you only have a correlation matrix
## from correlation matrix
CM = cor(iris[,1:4])
myPCA = princomp(covmat=CM)
myPCA
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
You get the same answer either way. If you want the loadings, they are stored in the myPCA structure.
myPCA$loadings
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Sepal.Length 0.521 0.377 0.720 0.261
Sepal.Width -0.269 0.923 -0.244 -0.124
Petal.Length 0.580 -0.142 -0.801
Petal.Width 0.565 -0.634 0.524
Comp.1 Comp.2 Comp.3 Comp.4
SS loadings 1.00 1.00 1.00 1.00
Proportion Var 0.25 0.25 0.25 0.25
Cumulative Var 0.25 0.50 0.75 1.00
answered Mar 9 at 2:28
G5WG5W
23.8k92344
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
add a comment |
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You can perform PCA in R with the princomp function. The documentation says that if you supply the argument covmat it will compute the principal components from the covariance matrix. But it also works to use this argument with the correlation matrix.
Here is a simple example using the iris data.
## principal components from the original data
princomp(iris[,1:4], cor=T)
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
Now suppose that you only have a correlation matrix
## from correlation matrix
CM = cor(iris[,1:4])
myPCA = princomp(covmat=CM)
myPCA
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
You get the same answer either way. If you want the loadings, they are stored in the myPCA structure.
myPCA$loadings
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Sepal.Length 0.521 0.377 0.720 0.261
Sepal.Width -0.269 0.923 -0.244 -0.124
Petal.Length 0.580 -0.142 -0.801
Petal.Width 0.565 -0.634 0.524
Comp.1 Comp.2 Comp.3 Comp.4
SS loadings 1.00 1.00 1.00 1.00
Proportion Var 0.25 0.25 0.25 0.25
Cumulative Var 0.25 0.50 0.75 1.00
answered Mar 9 at 2:28
G5WG5W
23.8k92344
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
add a comment |
You can perform PCA in R with the princomp function. The documentation says that if you supply the argument covmat it will compute the principal components from the covariance matrix. But it also works to use this argument with the correlation matrix.
Here is a simple example using the iris data.
## principal components from the original data
princomp(iris[,1:4], cor=T)
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
Now suppose that you only have a correlation matrix
## from correlation matrix
CM = cor(iris[,1:4])
myPCA = princomp(covmat=CM)
myPCA
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
You get the same answer either way. If you want the loadings, they are stored in the myPCA structure.
myPCA$loadings
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Sepal.Length 0.521 0.377 0.720 0.261
Sepal.Width -0.269 0.923 -0.244 -0.124
Petal.Length 0.580 -0.142 -0.801
Petal.Width 0.565 -0.634 0.524
Comp.1 Comp.2 Comp.3 Comp.4
SS loadings 1.00 1.00 1.00 1.00
Proportion Var 0.25 0.25 0.25 0.25
Cumulative Var 0.25 0.50 0.75 1.00
answered Mar 9 at 2:28
G5WG5W
23.8k92344
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
add a comment |
You can perform PCA in R with the princomp function. The documentation says that if you supply the argument covmat it will compute the principal components from the covariance matrix. But it also works to use this argument with the correlation matrix.
Here is a simple example using the iris data.
## principal components from the original data
princomp(iris[,1:4], cor=T)
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
Now suppose that you only have a correlation matrix
## from correlation matrix
CM = cor(iris[,1:4])
myPCA = princomp(covmat=CM)
myPCA
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
You get the same answer either way. If you want the loadings, they are stored in the myPCA structure.
myPCA$loadings
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Sepal.Length 0.521 0.377 0.720 0.261
Sepal.Width -0.269 0.923 -0.244 -0.124
Petal.Length 0.580 -0.142 -0.801
Petal.Width 0.565 -0.634 0.524
Comp.1 Comp.2 Comp.3 Comp.4
SS loadings 1.00 1.00 1.00 1.00
Proportion Var 0.25 0.25 0.25 0.25
Cumulative Var 0.25 0.50 0.75 1.00
answered Mar 9 at 2:28
G5WG5W
23.8k92344
You can perform PCA in R with the princomp function. The documentation says that if you supply the argument covmat it will compute the principal components from the covariance matrix. But it also works to use this argument with the correlation matrix.
Here is a simple example using the iris data.
## principal components from the original data
princomp(iris[,1:4], cor=T)
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
Now suppose that you only have a correlation matrix
## from correlation matrix
CM = cor(iris[,1:4])
myPCA = princomp(covmat=CM)
myPCA
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4
1.7083611 0.9560494 0.3830886 0.1439265
You get the same answer either way. If you want the loadings, they are stored in the myPCA structure.
myPCA$loadings
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4
Sepal.Length 0.521 0.377 0.720 0.261
Sepal.Width -0.269 0.923 -0.244 -0.124
Petal.Length 0.580 -0.142 -0.801
Petal.Width 0.565 -0.634 0.524
Comp.1 Comp.2 Comp.3 Comp.4
SS loadings 1.00 1.00 1.00 1.00
Proportion Var 0.25 0.25 0.25 0.25
Cumulative Var 0.25 0.50 0.75 1.00
answered Mar 9 at 2:28
G5WG5W
23.8k92344
edited Mar 9 at 11:52
answered Mar 9 at 2:28
G5WG5W
23.8k92344
answered Mar 9 at 2:28
G5WG5W
23.8k92344
answered Mar 9 at 2:28
G5WG5W
23.8k92344
23.8k92344
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
add a comment |
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
iris.csv is an original variable dataset. My problem is I only have the correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:27
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
Yes, but CM is just a correlation matrix.
– G5W
Mar 9 at 11:50
add a comment |
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Can you give a little more detail, maybe an example? eigendecomposition of a correlation matrix is (scaled) PCA ...
– Ben Bolker
Mar 9 at 2:26
Understood eigendecomposition of a correlation matrix is (scaled) PCA.
– Wilks
Mar 9 at 3:19
My problem is that the only data source is a correlation matrix. And I could not find the way to import it in R and manipulate it in R.
– Wilks
Mar 9 at 3:20
Below is my attempts:
– Wilks
Mar 9 at 3:42