This function gives the equivalent latent classes which have the same category success probabilities for each category or item.

LC2LG(Q, sequential = FALSE, att.str = NULL)

## Arguments

Q A required $$J \times K$$ binary Q-matrix. J represents test length and K represents the number of attributes of this test. Entry 1 at row j and column k represents the $$k^{th}$$ attribute is measured by item $$j$$, and 0 means item $$j$$ does not measure attribute $$k$$. logical; whether the Q-matrix is a Qc-matrix for sequential models? attribute structure. See GDINA for details.

## Value

An item or category by latent class matrix. In the G-DINA model, if item j measures $$Kj$$ attributes, $$2^K$$ latent classes can be combined into $$2^{Kj}$$ latent groups. This matrix gives which latent group each of $$2^K$$ latent classes belongs to for each item.

## Examples

attributepattern(3)#>      A1 A2 A3
#> [1,]  0  0  0
#> [2,]  1  0  0
#> [3,]  0  1  0
#> [4,]  0  0  1
#> [5,]  1  1  0
#> [6,]  1  0  1
#> [7,]  0  1  1
#> [8,]  1  1  1
q <- matrix(scan(text = "0 1 0 1 0 1 1 1 0"),ncol = 3)
q#>      [,1] [,2] [,3]
#> [1,]    0    1    1
#> [2,]    1    0    1
#> [3,]    0    1    0LC2LG(Q = q)#>      000 100 010 001 110 101 011 111
#> [1,]   1   1   2   3   2   3   4   4
#> [2,]   1   2   1   3   2   4   3   4
#> [3,]   1   1   2   1   2   1   2   2