BootFactorSCores Compute observation Bootstrap replicates of the factor scores from partial factor scores
BootFactorSCores Compute Bootstrap replicates of the factor scores
of the observations from partial factor scores.
The input is obtained from the distatis function,
the output is a 3-way array of dimensions number of observations by number of factors by number of replicates. The output is typically used to plot confidence intervals (i.e., ellipsoids or convex hulls) or to compute t-like statistic called bootstrap ratios.
BootFactorScores(PartialFS, niter = 1000)
PartialFS |
The partial factor scores (e.g., obtaiined from |
niter |
number of boostrap iterations (default = 1000) |
To compute a bootstrapped sample a set of K distance matrices is
selected with replacement from the original set of K distance matrices.
The partial factors scores of the selected distance matrices are then averaged
to produce the bootstrapped estimate of the factor scores of the observations.
This approach is also called partial boostrap by Lebart
(2007, see also Chateau & Lebart 1996).
It has the advantage of being
very fast even for very large data sets
Recent work (Cadoret & Husson, 2012), however,
suggests that partial boostrap could lead to optimistic bootstrap estimates when
the number of distance matrices is large and that
it is preferable to use instead
a total boostrap approach
(i.e., creating new compromises by resampling and then projecting them
on the common solution see function
BootFromCompromise,
and Cadoret & Husson, 2012 see also Abdi et al.,
2009 for an example).
the output is a 3-way array of dimensions "number of observations by number of factors by number of replicates."
Herve Abdi
Abdi, H., & Valentin, D., (2007). Some new and easy ways to describe, compare, and evaluate products and assessors. In D., Valentin, D.Z. Nguyen, L. Pelletier (Eds) New trends in sensory evaluation of food and non-food products. Ho Chi Minh (Vietnam): Vietnam National University-Ho chi Minh City Publishing House. pp. 5-18.
Abdi, H., Dunlop, J.P., & Williams, L.J. (2009). How to compute reliability estimates and display confidence and tolerance intervals for pattern classiffers using the Bootstrap and 3-way multidimensional scaling (DISTATIS). NeuroImage, 45, 89–95.
Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124–167.
These papers are available from www.utdallas.edu/~herve
Additional references:
Cadoret, M., Husson, F. (2012) Construction and evaluation of confidence ellipses applied at sensory data. Food Quality and Preference, 28, 106–115.
Chateau, F., & Lebart, L. (1996). Assessing sample variability in the visualization techniques related to principal component analysis: Bootstrap and alternative simulation methods. In A. Prats (Ed.),Proceedings of COMPSTAT 2006. Heidelberg: Physica Verlag.
Lebart, L. (2007). Which bootstrap for principal axes methods? In Selected contributions in data analysis and classification, COMPSTAT 2006. Heidelberg: Springer Verlag.
# 1. Load the Sort data set from the SortingBeer example (available from the DistatisR package) data(SortingBeer) # Provide an 8 beers by 10 assessors set of results of a sorting task #----------------------------------------------------------------------------- # 2. Create the set of distance matrices (one distance matrix per assessor) # (ues the function DistanceFromSort) DistanceCube <- DistanceFromSort(Sort) #----------------------------------------------------------------------------- # 3. Call the DISTATIS routine with the cube of distance as parameter testDistatis <- distatis(DistanceCube) # The factor scores for the beers are in # testDistatis$res4Splus$F # the partial factor score for the beers for the assessors are in # testDistatis$res4Splus$PartialF # # 4. Get the bootstraped factor scores (with default 1000 iterations) BootF <- BootFactorScores(testDistatis$res4Splus$PartialF)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.