print.FuncompCGL {Compack} R Documentation

## Print a `"FuncompCGL"` object.

### Description

print the number of nonzero coefficient curves for the functional compositional predictors at each `lam` along the FuncompCGL path.

### Usage

```## S3 method for class 'FuncompCGL'
print(x, digits = max(3, getOption("digits") - 3), ...)
```

### Arguments

 `x` fitted `FuncompCGL` object. `digits` significant digits in printout. `...` not used.

### Value

a two-column matrix; the first column `DF` gives the number of nonzero coefficients and the second column `Lam` gives the correspondint `lam` values.

### Author(s)

Zhe Sun and Kun Chen

### References

Sun, Z., Xu, W., Cong, X., Li G. and Chen K. (2020) Log-contrast regression with functional compositional predictors: linking preterm infant's gut microbiome trajectories to neurobehavioral outcome, https://arxiv.org/abs/1808.02403 Annals of Applied Statistics

`FuncompCGL`, and `coef`, `predict` and `plot` methods for `"FuncompCGL"` object.

### Examples

```df_beta = 5
p = 30
beta_C_true = matrix(0, nrow = p, ncol = df_beta)
beta_C_true[1, ] <- c(-0.5, -0.5, -0.5 , -1, -1)
beta_C_true[2, ] <- c(0.8, 0.8,  0.7,  0.6,  0.6)
beta_C_true[3, ] <- c(-0.8, -0.8 , 0.4 , 1 , 1)
beta_C_true[4, ] <- c(0.5, 0.5, -0.6  ,-0.6, -0.6)
Data <- Fcomp_Model(n = 50, p = p, m = 2, intercept = TRUE,
SNR = 2, sigma = 2, rho_X = 0, rho_T = 0.5, df_beta = df_beta,
n_T = 20, obs_spar = 1, theta.add = FALSE,
beta_C = as.vector(t(beta_C_true)))
m1 <- FuncompCGL(y = Data\$data\$y, X = Data\$data\$Comp ,
Zc = Data\$data\$Zc, intercept = Data\$data\$intercept,
k = df_beta)
print(m1)

```

[Package Compack version 0.1.0 Index]