Description Usage Arguments Details Value Author(s) References See Also Examples
This function makes prediction based on a "GIC.compCL" model,
using the stored "compCL.fit" object and the optimal value of lambda.
1 2 |
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 101 785-979.
GIC.compCL and compCL, and
coef and
plot methods for "GIC.compCL".
1 2 3 4 5 6 7 8 9 10 11 12 | 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")
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