performanceBLB {BiplotML} R Documentation

## Performance comparison of severals estimation algorithms

### Description

This function computes the estimates of A and B matrix with severals algorithms.

### Usage

```performanceBLB(xi, k = 2, L = 0, method = NULL, maxit = NULL)
```

### Arguments

 `xi` Binary matrix. `k` Dimensions number. By default `k = 2`. `L` Penalization parameter. By default `L = 0`. `method` use value 1 for algorithms without gradient, 2 with gradient, 3 quasi-newton methods or 4 for all methods. By default `method = 2`. `maxit` The maximum number of iterations. Defaults to 100 for the gradient methods, and 500 without gradient.

### Details

This function compare the process time and convergence of different algorithms without gradient, with gradient or quasi-newton method for estimating the parameters in a Binary Logistic Biplot

### Value

data frame with method, time of process, convergence and number of evaluations

### Author(s)

Giovany Babativa <gbabativam@gmail.com>

### References

John C. Nash (2011). Unifying Optimization Algorithms to Aid Software System Users:optimx for R. Journal of Statistical Software. 43(9). 1–14.

John C. Nash (2014). On Best Practice Optimization Methods in R. Journal of Statistical Software. 60(2). 1–14.

Vicente-Villardon, J.L. and Galindo, M. Purificacion (2006), Multiple Correspondence Analysis and related Methods. Chapter: Logistic Biplots. Chapman-Hall

### See Also

`gradientDesc`

### Examples

```
data('Methylation')
set.seed(123456)
########### Gradient Methods
performanceBLB(xi = Methylation)
performanceBLB(xi = Methylation, maxit = 150)

########### Without Gradient Methods
performanceBLB(xi = Methylation, method = 1)
performanceBLB(xi = Methylation, method = 1, maxit = 100)

########### Quasi-Newton Methods
performanceBLB(xi = Methylation, method = 3)
performanceBLB(xi = Methylation, method = 3, maxit = 100)

########### All methods
performanceBLB(x = Methylation, method = 4)

```

[Package BiplotML version 1.0.1 Index]