For 129 laboratory samples of fossil fuels the heat value and the humidity were
determined together with two spectra.
One spectrum is ultraviolet-visible (UV-VIS), measured at 1335 wavelengths in
the range of 250.4 to 878.4 nanometer (nm), the other a near infrared spectrum
(NIR) measured at 2307 wavelengths in the range of 800.4 to 2779.0 nm.
`fuelSubset`

is a subset of the original dataset containing only 10% of
the original measures of the spectra, resulting in 231 measures of the
NIR spectrum and 134 measures of the UVVIS spectrum.

`data("fuelSubset")`

A data list with 129 observations on the following 7 variables.

`heatan`

heat value in mega joule (mJ)

`h2o`

humidity in percent

`NIR`

near infrared spectrum (NIR)

`UVVIS`

ultraviolet-visible spectrum (UV-VIS)

`nir.lambda`

wavelength of NIR spectrum in nm

`uvvis.lambda`

wavelength of UV-VIS spectrum in nm

`h2o.fit`

predicted values of humidity

The aim is to predict the heat value using the spectral data. The variable
`h2o.fit`

was generated by a functional linear regression model, using
both spectra and their derivatives as predictors.

# NOT RUN { data("fuelSubset", package = "FDboost") ## center the functional covariates per observed wavelength fuelSubset$UVVIS <- scale(fuelSubset$UVVIS, scale = FALSE) fuelSubset$NIR <- scale(fuelSubset$NIR, scale = FALSE) ## to make mboost::df2lambda() happy (all design matrix entries < 10) ## reduce range of argvals to [0,1] to get smaller integration weights fuelSubset$uvvis.lambda <- with(fuelSubset, (uvvis.lambda - min(uvvis.lambda)) / (max(uvvis.lambda) - min(uvvis.lambda) )) fuelSubset$nir.lambda <- with(fuelSubset, (nir.lambda - min(nir.lambda)) / (max(nir.lambda) - min(nir.lambda) )) ### fit mean regression model with 100 boosting iterations, ### step-length 0.1 and mod <- FDboost(heatan ~ bsignal(UVVIS, uvvis.lambda, knots=40, df=4, check.ident=FALSE) + bsignal(NIR, nir.lambda, knots=40, df=4, check.ident=FALSE), timeformula = NULL, data = fuelSubset) summary(mod) ## plot(mod) # }