Description Usage Arguments Details Value Author(s) References See Also Examples
get the coefficients at the requested values for lam
from a fitted FuncompCGL
object.
1 2 |
object |
fitted |
s |
value(s) of the penalty parameter |
... |
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) 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 predict
,
plot
and print
methods for "FuncompCGL"
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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)
coef(m1)
coef(m1, s = c(0.5, 0.1, 0.01))
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