plot.cv.FuncompCGL {Compack}  R Documentation 
"cv.FuncompCGL"
.Plot the crossvalidation curve with its upper and lower standard deviation curves.
## S3 method for class 'cv.FuncompCGL' plot(x, xlab = c("log", "log", "lambda"), trim = FALSE, k, ...)
x 
fitted 
xlab 
what is on the Xaxis,

trim 
logical; whether to use the trimmed result.
Default is 
k 
a vector or character string

... 
other graphical parameters. 
A crossvalidation curve is produced.
No return value. Side effect is a base R plot.
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
cv.FuncompCGL
and FuncompCGL
, and
predict
and
coef
methods for "cv.FuncompCGL"
object.
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))) k_list < 4:5 cv_m1 < cv.FuncompCGL(y = Data$data$y, X = Data$data$Comp, Zc = Data$data$Zc, intercept = Data$data$intercept, k = k_list, nfolds = 5, keep = TRUE) plot(cv_m1) plot(cv_m1, xlab = "log", k = k_list)