In an experimental setting the viscosity of resin was measured over time to asses the curing process depending on 5 binary factors (low-high).

`data("viscosity")`

A data list with 64 observations on the following 7 variables.

`visAll`

viscosity measures over all available time points

`timeAll`

time points of viscosity measures

`T_C`

temperature of tools

`T_A`

temperature of resin

`T_B`

temperature of curing agent

`rspeed`

rotational speed

`mflow`

mass flow

The aim is to determine factors that affect the curing process in the mold. The desired viscosity-curve has low values in the beginning followed by a sharp increase. Due to technical reasons the measuring method of the rheometer has to be changed in a certain range of viscosity. The first observations are measured by rotation of a blade giving observations every two seconds, the later observations are measured through oscillation of a blade giving observations every ten seconds. In the later observations the resin is quite hard so the measurements should be interpreted as a qualitative measure of hardening.

# NOT RUN { 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 median regression model with 100 boosting iterations, ## step-length 0.4 and smooth time-specific offset ## the factors are in effect coding -1, 1 for the levels mod <- FDboost(vis ~ 1 + bols(T_C, contrasts.arg = "contr.sum", intercept=FALSE) + bols(T_A, contrasts.arg = "contr.sum", intercept=FALSE), timeformula=~bbs(time, lambda=100), numInt="equal", family=QuantReg(), offset=NULL, offset_control = o_control(k_min = 9), data=viscosity, control=boost_control(mstop = 100, nu = 0.4)) summary(mod) # }