predict.cv.compCL {Compack}  R Documentation 
"cv.compCL"
object.This function makes prediction based on a crossvalidated compCL
model,
using the stored compCL.fit
object.
## S3 method for class 'cv.compCL' predict(object, Znew, Zcnew = NULL, s = c("lam.min", "lam.1se" ), trim = FALSE, ...)
object 
fitted 
Znew 

Zcnew 

s 
specify the

trim 
Whether to use the trimmed result. Default is FASLE. 
... 
not used. 
s
is the vector at which predictions are requested. If s
is not in the lambda
sequence used for fitting the model, the predict
function uses linear interpolation.
predicted values at the requested values of s
.
Zhe Sun and Kun Chen
Lin, W., Shi, P., Peng, R. and Li, H. (2014) Variable selection in regression with compositional covariates, https://academic.oup.com/biomet/article/101/4/785/1775476. Biometrika 101 785979.
cv.compCL
and compCL
,
and coef
and plot
methods
for "cv.compCL"
object.
p = 30 n = 50 beta = c(1, 0.8, 0.6, 0, 0, 1.5, 0.5, 1.2) beta = c( beta, rep(0, times = p  length(beta)) ) Comp_data = comp_Model(n = n, p = p, beta = beta, intercept = FALSE) test_data = comp_Model(n = 30, p = p, beta = beta, intercept = FALSE) cvm1 < cv.compCL(y = Comp_data$y, Z = Comp_data$X.comp, Zc = Comp_data$Zc, intercept = Comp_data$intercept) y_hat = predict(cvm1, Znew = test_data$X.comp, Zcnew = test_data$Zc) predmat = predict(cvm1, Znew = test_data$X.comp, Zcnew = test_data$Zc, s = NULL) plot(test_data$y, y_hat, xlab = "Observed response", ylab = "Predicted response") abline(a = 0, b = 1, col = "red")