Constrained row tensor product
bl1 %Xc% bl2
base-learner 1, e.g.
base-learner 2, e.g.
Combining single base-learners to form new, more complex base-learners, with an identifiability constraint to center the interaction around the intercept and around the two main effects. Suitable for functional response.
%X% in package mboost, see
a row tensor product of linear base-learners is returned by
%Xc% applies a sum-to-zero constraint to the design matrix suitable for
functional response if an interaction of two scalar covariates is specified
in the case that the model contains a global intercept and both main effects,
as the interaction is centerd around the intercept and centered around the two main effects.
See Web Appendix A of Brockhaus et al. (2015) for details on how to enforce the constraint
for the functional intercept.
Use e.g. in a model call to
FDboost, following the scheme,
y ~ 1 + bolsc(x1) + bolsc(x2) + bols(x1) %Xc% bols(x2),
1 induces a global intercept and
x2 are factor variables.
Sarah Brockhaus, David Ruegamer
Brockhaus, S., Scheipl, F., Hothorn, T. and Greven, S. (2015): The functional linear array model. Statistical Modelling, 15(3), 279-300.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
######## Example for function-on-scalar-regression with interaction effect of two scalar covariates data("viscosity", package = "FDboost") ## set time-interval that should be modeled interval <- "101" ## model time until "interval" and take log() of viscosity end <- which(viscosity$timeAll == as.numeric(interval)) viscosity$vis <- log(viscosity$visAll[,1:end]) viscosity$time <- viscosity$timeAll[1:end] # with(viscosity, funplot(time, vis, pch = 16, cex = 0.2)) ## fit model with interaction that is centered around the intercept ## and the two main effects mod1 <- FDboost(vis ~ 1 + bolsc(T_C, df=1) + bolsc(T_A, df=1) + bols(T_C, df=1) %Xc% bols(T_A, df=1), timeformula = ~bbs(time, df=6), numInt = "equal", family = QuantReg(), offset = NULL, offset_control = o_control(k_min = 9), data = viscosity, control=boost_control(mstop = 100, nu = 0.4)) ## check centering around intercept colMeans(predict(mod1, which = 4)) ## check centering around main effects colMeans(predict(mod1, which = 4)[viscosity$T_A == "low", ]) colMeans(predict(mod1, which = 4)[viscosity$T_A == "high", ]) colMeans(predict(mod1, which = 4)[viscosity$T_C == "low", ]) colMeans(predict(mod1, which = 4)[viscosity$T_C == "low", ]) ## find optimal mstop using cvrsik() or validateFDboost() ## ... ## look at interaction effect in one plot # funplot(mod1$yind, predict(mod1, which=4))
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.