# %Xc%

0th

Percentile

##### Constrained row tensor product

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.

##### Usage
bl1 %Xc% bl2
##### Arguments
bl1

base-learner 1, e.g. bols(x1)

bl2

base-learner 2, e.g. bols(x2)

##### Details

Similar to %X% in package mboost, see %X%, a row tensor product of linear base-learners is returned by %Xc%. %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), where 1 induces a global intercept and x1, x2 are factor variables, see Ruegamer et al. (2018).

##### References

Brockhaus, S., Scheipl, F., Hothorn, T. and Greven, S. (2015): The functional linear array model. Statistical Modelling, 15(3), 279-300.

Ruegamer D., Brockhaus, S., Gentsch K., Scherer, K., Greven, S. (2018). Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 621-642.

• %Xc%
##### Examples
# NOT RUN {

######## 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))

# }

Documentation reproduced from package FDboost, version 0.3-2, License: GPL-2

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