coef.GIC.compCL {Compack} R Documentation

## Extracts estimated coefficients from a `"GIC.compCL"` object.

### Description

This function gets coefficients from a `compCL` object, using the stored `"compCL.fit"` object.

### Usage

```## S3 method for class 'GIC.compCL'
coef(object, s = "lam.min", ...)
```

### Arguments

 `object` fitted `"GIC.compCL"` object. `s` value(s) of the penalty parameter `lam` at which coefficients are requested. `s="lam.min"` (default) stored in the `GIC.compCL` object, which gives value of `lam` that achieves the minimum value of GIC. If `s` is numeric, it is taken as the value(s) of `lam` to be used. If `s = NULL`, the whole sequence of `lam` stored in the `GIC.compCGL` object is used. `...` not used.

### Details

`s` is a vector of lambda values at which the coefficients are requested. If `s` is not in the `lam` sequence used for fitting the model, the `coef` function will use linear interpolation, so the function should be used with caution.

### Value

The coefficients at the requested tuning parameter values in `s`.

### Author(s)

Zhe Sun and Kun Chen

### References

Lin, W., Shi, P., Peng, R. and Li, H. (2014) Variable selection in regression with compositional covariates, https://academic.oup.com/biomet/article/101/4/785/1775476. Biometrika 101 785-979.

### See Also

`GIC.compCL` and `compCL`, and `predict`, and `plot` methods for `"GIC.compCL"` object.

### Examples

```p = 30
n = 50
beta = c(1, -0.8, 0.6, 0, 0, -1.5, -0.5, 1.2)
beta = c(beta, rep(0, times = p - length(beta)))
Comp_data = comp_Model(n = n, p = p, beta = beta, intercept = FALSE)
GICm1 <- GIC.compCL(y = Comp_data\$y, Z = Comp_data\$X.comp, Zc = Comp_data\$Zc,
intercept = Comp_data\$intercept)
coef(GICm1)
coef(GICm1, s = c(1, 0.5, 0.1))

```

[Package Compack version 0.1.0 Index]