# plot.GIC.compCL: Plot the GIC curve produced by '"GIC.compCL"' object. In Compack: Regression with Compositional Covariates

## Description

Plot the CIC curve as a function of the `lam` values.

## Usage

 ```1 2``` ```## S3 method for class 'GIC.compCL' plot(x, xlab = c("log", "-log", "lambda"), ...) ```

## Arguments

 `x` fitted `"GIC.compCL"` object. `xlab` what is on the X-axis, `"log"` plots against `log(lambda)` (default), `"-log"` against `-log(lambda)`, and `"lambda"` against `lambda`. `...` other graphical parameters.

## Details

A GIC curve is produced.

## Value

No return value. Side effect is a base R plot.

## 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.

`GIC.compCL` and `compCL`, and `predict` and `coef` methods for `"GIC.compCL"` object.
 ```1 2 3 4 5 6 7 8 9``` ```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) plot(GICm1) plot(GICm1, xlab = "-log") ```