Description Usage Arguments Value Author(s) References See Also Examples

print the number of nonzero coefficient curves for the functional compositional
predictors at each `lam`

along the FuncompCGL path.

1 2 |

`x` |
fitted |

`digits` |
significant digits in printout. |

`...` |
not used. |

a two-column matrix; the first column `DF`

gives the number of nonzero
coefficients and
the second column `Lam`

gives the correspondint `lam`

values.

Zhe Sun and Kun Chen

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
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)
``` |

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