Biscuit: Biscuit dough piece data
In fds: Functional Data Sets

Description Usage Format Details Note References Examples

The experiment involved varying the composition of biscuit dough pieces. Two sets of dough pieces were measured, a calibration set and a prediction set. They were created and measured as two distinct sets, on separate occasions, and do not result from a random (or any other) split of a larger set.

data(labp)
data(labc)
data(nirp)
data(nirc)

nirp and nirc are objects of class fds.

labp and labc are objects of class matrix.

The data labc (c stands for calibration) and labp (p stands for prediction) contain the reference data on the composition of the doughs.

The data nirc and nirp contain 700 point near infrared reflectance (NIR) spectra for the same dough. The spectral range is 1100-2498 nm in steps of 2nm.

The data labc$y is 4 rows by 40 columns, the rows being fat, sucrose, flour and water all in percents. The percents do not quite add up to 100, since there are other minor ingredients present, but they add up to nearly 100 percent.

According to Brown et al. (2001), the observation 23 in the calibration set appears as an outlier.

Sample number 21 in the labp shows up as a validation set.

We thank Professor Marina Vannucci for the permission to re-distribute this data set.

P. J. Brown and T. Fearn and M. Vannucci (2001) "Bayesian wavelet regression on curves with applications to a spectroscopic calibration problem", Journal of the American Statistical Association, 96(454), pp. 398-408.

1 2	plot(nirp) plot(nirc)

Loading required package: rainbow
Loading required package: MASS
Loading required package: pcaPP
Loading required package: RCurl
Loading required package: bitops
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found