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ttnphns
ttnphns
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$^1$ Note from @ttnphns: I suppose this software does correspondensecorrespondence analysis, not just arbitrary biplot. CA is very often used in brand research.

$^1$ Note from @ttnphns: I suppose this software does correspondense analysis, not just arbitrary biplot. CA is very often used in brand research.

$^1$ Note from @ttnphns: I suppose this software does correspondence analysis, not just arbitrary biplot. CA is very often used in brand research.

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ttnphns
ttnphns
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There is a software called Brandmap$^1$ which can return a biplot from a matrix. I am trying to run the same result in R but the coordinates are not the same.

I found that the values in the right vector are the same but with negative sign and all the values in the left vector are multiplied by -2.918. I am not sure if there is any weighting in the calculation of that software. What kind of adjustment I can try so that I can run the same results in R?

$^1$ Note from @ttnphns: I suppose this software does correspondense analysis, not just arbitrary biplot. CA is very often used in brand research.

There is a software called Brandmap which can return a biplot from a matrix. I am trying to run the same result in R but the coordinates are not the same.

I found that the values in the right vector are the same but with negative sign and all the values in the left vector are multiplied by -2.918. I am not sure if there is any weighting in the calculation of that software. What kind of adjustment I can try so that I can run the same results in R?

There is a software called Brandmap$^1$ which can return a biplot from a matrix. I am trying to run the same result in R but the coordinates are not the same.

I found that the values in the right vector are the same but with negative sign and all the values in the left vector are multiplied by -2.918. I am not sure if there is any weighting in the calculation of that software. What kind of adjustment I can try so that I can run the same results in R?

$^1$ Note from @ttnphns: I suppose this software does correspondense analysis, not just arbitrary biplot. CA is very often used in brand research.

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bbbbbliu
bbbbbliu
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I want to run a same result of biplot in R

There is a software called Brandmap which can return a biplot from a matrix. I am trying to run the same result in R but the coordinates are not the same.

First I input a simple matrix into the software.

  x1 x2 x3
a  6  3  7
b  8  6  7
c  9  4  2

There were several options of data centering and I chose to center the data by subtracting every number in the matrix from its (row mean*column mean/grand mean).

         x1    x2         x3
a -1.076923 -1.00  2.0769231
b -1.288462  0.75  0.5384615
c  2.365385  0.25 -2.6153846

Then I chose column factorization to create a biplot. I guess it means a covariance biplot which the singular values are totally assigned to the right singular vectors.

It showed the coordinates:

     dim1    dim2
x1  -2.81   -0.73
x2  -0.55    1.15
x3   3.36   -0.42
        
a    1.58   -1.78
b    0.75    2.26
c   -2.33   -0.48

I tried to calculate the same results in R.

> P = matrix(c(6,8,9,3,6,4,7,7,2),nrow=3)
> row.names(P)=c("a","b","c")
> colnames(P)=c("x1","x2","x3")
> P
  x1 x2 x3
a  6  3  7
b  8  6  7
c  9  4  2
> r1 = matrix(rep(1,3))     #row sum
> c1 = matrix(rep(1,3))     #column sum
> r = P%*%r1
> c = t(P)%*%c1
> L = P - r%*%t(c)/sum(P)   #subtract row mean*column mean/grand mean
> L
         x1    x2         x3
a -1.076923 -1.00  2.0769231
b -1.288462  0.75  0.5384615
c  2.365385  0.25 -2.6153846
> S = svd(L)
> S$v%*%diag(S$d)
           [,1]       [,2]         [,3]
[1,]  2.8077724  0.7289408 -8.10596e-17
[2,]  0.5487104 -1.1506159 -8.10596e-17
[3,] -3.3564829  0.4216750 -8.10596e-17
> S$u
           [,1]       [,2]      [,3]
[1,] -0.5420705  0.6105950 0.5773503
[2,] -0.2577555 -0.7747443 0.5773503
[3,]  0.7998260  0.1641494 0.5773503

I found that the values in the right vector are the same but with negative sign and all the values in the left vector are multiplied by -2.918. I am not sure if there is any weighting in the calculation of that software. What kind of adjustment I can try so that I can run the same results in R?