Description Usage Arguments Details Value Author(s) References See Also Examples
This function gets coefficients from a "GIC.FuncompCGL"
object,
using the stored "FuncompCGL.fit"
object, and the optimal values of
lam
and k
.
1 2 
object 
fitted 
s 
value(s) of the regularization parameter

k 
value(s) of degrees of freedom of the basis function at which coefficents are requested.

... 
not used. 
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.
The coefficients at the requested tuning parameter values in s
.
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
plot
methods for "GIC.FuncompCGL"
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  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)
n = 50
k_list < c(4,5)
Data < Fcomp_Model(n = n, 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)))
GIC_m1 < GIC.FuncompCGL(y = Data$data$y, X = Data$data$Comp,
Zc = Data$data$Zc, intercept = Data$data$intercept,
k = k_list)
coef(GIC_m1)
coef(GIC_m1, s = c(0.05, 0.01), k = c(4,5))
coef(GIC_m1, s = NULL, k = c(4,5))

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