Description Usage Arguments Details Value Author(s) References See Also Examples
Plot the GIC curve as a function of the lam
values used for different
degree of freedom k
.
1 2 
x 
fitted 
xlab 
what is on the Xaxis,

k 
value(s) of the degrees of freedom at which GIC cuvre(s) are plotted.

... 
other graphical parameters. 
A GIC curve is produced.
No return value. Side effect is a base R plot.
Zhe Sun and Kun Chen
Sun, Z., Xu, W., Cong, X., Li G. and Chen K. (2020) Logcontrast 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
GIC.FuncompCGL
and FuncompCGL
, and
predict
and
coef
methods for "GIC.FuncompCGL"
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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 = 0, intercept = TRUE,
SNR = 4, sigma = 3, rho_X = 0.6, rho_T = 0,
df_beta = df_beta, n_T = 20, obs_spar = 1, theta.add = FALSE,
beta_C = as.vector(t(beta_C_true)))
k_list < c(4,5)
GIC_m1 < GIC.FuncompCGL(y = Data$data$y, X = Data$data$Comp,
Zc = Data$data$Zc, intercept = Data$data$intercept,
k = k_list)
plot(GIC_m1)
plot(GIC_m1, xlab = "log", k = k_list)

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