Description Usage Arguments Details Value Author(s) References See Also Examples
This function gets the coefficients from a cross-validated
FuncompCGL
model, using the stored "FuncompCGL.fit"
object,
and the optimal grid values of the penalty parameter lam
and the degrees of freedom k
.
1 2 |
object |
fitted |
trim |
logical; whether to use the trimmed result. Default is |
s |
value(s) of the penalty parameter
|
k |
value(s) of the 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 values of s
and k
.
If k
is a vector, a list of coefficient matrices is returned.
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
cv.FuncompCGL
and FuncompCGL
, and
predict
and
plot
methods for "cv.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)
Data <- Fcomp_Model(n = 50, p = p, m = 0, intercept = TRUE,
SNR = 4, sigma = 3, rho_X = 0, rho_T = 0.6, df_beta = df_beta,
n_T = 20, obs_spar = 1, theta.add = FALSE,
beta_C = as.vector(t(beta_C_true)))
cv_m1 <- cv.FuncompCGL(y = Data$data$y, X = Data$data$Comp,
Zc = Data$data$Zc, intercept = Data$data$intercept,
k = c(4,5), nfolds = 5, nlam = 50,
keep = TRUE)
coef(cv_m1)
coef(cv_m1, s = "lam.1se")
coef(cv_m1, s = c(0.5, 0.1, 0.05), k = c(4,5))
coef(cv_m1, s = NULL, k = c(4,5))
|
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