predict.GIC.compCL {Compack}  R Documentation 
"GIC.compCL"
object.This function makes prediction based on a "GIC.compCL"
model,
using the stored "compCL.fit"
object and the optimal value of lambda
.
## S3 method for class 'GIC.compCL' predict(object, Znew, Zcnew = NULL, s = "lam.min", ...)
object 
fitted 
Znew 

Zcnew 

s 
specify the

... 
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.
GIC.compCL
and compCL
, and
coef
and
plot
methods for "GIC.compCL"
.
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 = 100, p = p, beta = beta, intercept = FALSE) GICm1 < GIC.compCL(y = Comp_data$y, Z = Comp_data$X.comp, Zc = Comp_data$Zc, intercept = Comp_data$intercept) y_hat = predict(GICm1, Znew = test_data$X.comp, Zcnew = test_data$Zc) predmat = predict(GICm1, Znew = test_data$X.comp, Zcnew = test_data$Zc, s = c(1, 0.5, 1)) plot(test_data$y, y_hat, xlab = "Observed value", ylab = "Predicted value") abline(a = 0, b = 1, col = "red")