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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