gendataWmm {lazy.procrustes} R Documentation

## Generation of Procscal Data

### Description

Generation of Procscal Data

### Usage

```gendataWmm(ns, nv, nr, genm = 0, genW = 0, genT = 1, genW2 = 0,
genmu = 0, mu0 = 0, genD1 = 0, genD2 = 0, eqm = 0, eqW = 0,
eqT = 0, eqW2 = 0, diagW2 = 1, eqD1 = 0, eqD2 = 0, genV = 0,
diagV = 1, pcA = 0, errstd = 0, pmiss = 0, minWD = 0.2)
```

### Arguments

 `ns` # of matrices to be generated. `nv` # of rows of the matrices to be rotated. `nr` # of columns of the matrices to be rotated. `genm` = 1 to generate m vectors, the column mean `genW` = 1 to generate scalar W matrices, the left weight = 2 to generate diagonal W matrices, the left weight `genT` = 0 to skip the generation of T matrices, the rotation `genW2` = 1 to generate W2 matrices, the right weigt `genmu` = 1 to generate mu matrix `mu0` The common value of mu `genD1` = 1 to generate D1 matrices, `genD2` = 1 to generate D2 matrices, `eqm` = 1 to generate common m `eqW` = 1 to generate common W `eqT` = 1 to generate common T `eqW2` = 1 to generate common W2 `diagW2` = 0 to make W2 non-diagonal `eqD1` = 1 to generate common WD1 `eqD2` = 1 to generate common D2 `genV` = 1 to generate V matrix `diagV` = 0 to make V non-diagonal `pcA` = 1 to make the column variances of A equal = 2 to column orthogonalize A `errstd` std of errors to be added to final C matrix `pmiss` = proportion of missing elements of C matrix `minWD` = minimum value of W and D matrices

### Details

This program generates A, mk, Tk, Wk, W2k, D1k, D2k matrices
so that the k-th target matrix Ck is created as
Ck = inv(D1k) ( A Wk Tk W2k + 1 mk' ) inv(D2k) + Ek, k=1,2,...,ns,
where Disp(Ek) = errstd^2*solve(V).

When genmu=1, we set genm=0; genD1=0; genD2=0; genW2=0; genV=0 and
muk = mu0 + A uk
will be generated via uk where muk is nv x 1 and uk is nr x 1.

For examples, see procotWKS or procscal.

### Value

A list of all the matrices and the parameter values.

[Package lazy.procrustes version 0.1.3 Index]