Description Usage Arguments Details Value Author(s) References See Also Examples
Plot the cross-validation curve with its upper and lower standard deviation curves.
1 2 |
x |
fitted |
xlab |
what is on the X-axis,
|
trim |
logical; whether to use the trimmed result.
Default is |
k |
a vector or character string
|
... |
other graphical parameters. |
A cross-validation 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) 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
coef
methods for "cv.FuncompCGL"
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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)
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