gradientDesc | R Documentation |
This function computes the parameters of A and B in Binary Logistic Biplot under algorithm of Descendent Gradient.
gradientDesc( x, k = 2, rate = 0.001, converg = 0.001, max_iter, plot = FALSE, ... )
x |
Binary matrix. |
k |
Dimensions number. By default |
rate |
The value of the rate of descent α in the algorithm of descending gradient. By default α = 0.001. |
converg |
Tolerance limit to achieve convergence. By default |
max_iter |
Maximum iterations number. |
plot |
Plot the Logistic Biplot. |
... |
other arguments |
We note that the Binary Logistic Biplot is defined as:
logit(π_{ij})= log≤ft( \frac{π_{ij}}{1-π_{ij}} \right)=μ_{j}+∑_{s=1}^kb_{js}a_{is} = μ_{j}+\mathbf{a_i^{T}b_j}
Also, note that the gradient is:
\nabla \ell= ≤ft(\frac{\partial \ell}{\partial μ}, \frac{\partial \ell}{\partial \mathbf{A}}, \frac{\partial \ell}{\partial \mathbf{B}}\right)== ≤ft( (Π - \mathbf{X})^T, (Π - \mathbf{X})\mathbf{B}, (Π - \mathbf{X})^TA \right)
The coefficients of A and B matrix.
Giovany Babativa <gbabativam@gmail.com>
Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall
plotBLB, performanceBLB
data('Methylation') set.seed(02052020) MatGD <- gradientDesc(x = Methylation, k=2, max_iter=10000) outGD <- gradientDesc(x = Methylation, k=2, max_iter=10000, plot = TRUE)
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