predict.GIC.compCL {Compack}R Documentation

Make predictions based on a "GIC.compCL" object.


This function makes prediction based on a "GIC.compCL" model, using the stored "" object and the optimal value of lambda.


## S3 method for class 'GIC.compCL'
predict(object, Znew, Zcnew = NULL, s = "lam.min", ...)



fitted "GIC.compCL" model.


z matrix as in compCL with new compositional data or categorical data.


Zc matrix as in compCL with new data for other covariates. Default is NULL


specify the lam at which prediction(s) is requested.

  • s = "lam.min" (default), lam that obtains the minimun value of GIC values.

  • if s is numeric, it is taken as the value(s) of lam to be used.

  • if s = NULL, uses the whole sequence of lam stored in the "GIC.compCL" object.


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, Biometrika 101 785-979.

See Also

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")

[Package Compack version 0.1.0 Index]