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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