print.FuncompCGL: Print a '"FuncompCGL"' object.

In jiji6454/compReg: Regression with Compositional Covariates

Description

print the number of nonzero coefficient curves for the functional compositional predictors at each lam along the FuncompCGL path.

Usage

## S3 method for class 'FuncompCGL'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	fitted FuncompCGL object.
digits	significant digits in printout.
...	not used.

Value

a two-column matrix; the first column DF gives the number of nonzero coefficients and the second column Lam gives the correspondint lam values.

Author(s)

Zhe Sun and Kun Chen

References

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

See Also

FuncompCGL, and coef, predict and plot methods for "FuncompCGL" object.

Examples

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)

jiji6454/compReg documentation built on Feb. 5, 2021, 2:20 p.m.