projectVoc | R Documentation |
distatis
-type
of analysis.projectVoc
Compute barycentric projection for count-like
description of the items of a distatis
-type of analysis.
The data need to be non-negative and typically represent
the vocabulary (i.e., words) used to describe the items
in a sorting/ranking/projective-mapping task.
projectVoc(CT.voc, Fi, namesOfFactors = NULL)
CT.voc |
a matrix or data.frame
storing a
I items by J descriptors
contingency table where the i,j-th cell
gives the number of times
the j-th descriptor (in the column)
was used to describe the i-th item
(in the row). |
Fi |
a matrix or data.frame
storing the
I items by L factor scores obtained
from the compromise of a |
namesOfFactors |
(Default: NULL), if |
two types of projection are computed: 1) a plain barycentric (words are positioned at the barycenter–a.k.a. center of mass–of the items it describes) and 2) a correspondence analysis barycentric where the variance of the projected words is equal to the variance of the items (as for correspondence analysis when using the "symmetric" representation).
a list with
1) Fvoca.bary
: the barycentric projections of
the words,
and 2) Fvoca.normed
: the CA normalized
(i.e., variance of projections equals eigenvalue)
barycentric projections of
the words.
Herve Abdi
Abdi, H, & Valentin, D. (2007). Papers available from https://personal.utdallas.edu/~herve/
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.
and
Lahne, J., Abdi, H., & Heymann, H. (2018). Rapid sensory profiles with DISTATIS and barycentric text projection: An example with amari, bitter herbal liqueurs. Food Quality and Preference, 66, 36-43.
# use the data from the BeersProjectiveMapping dataset data("BeersProjectiveMapping") # Create the I*J*K brick of data zeBrickOfData <- projMap2Cube( BeersProjectiveMapping$ProjectiveMapping, shape = 'flat', nVars = 2) # create the cube of covariance matrices between beers cubeOfCov <- createCubeOfCovDis(zeBrickOfData$cubeOfData) # Call distatis testDistatis <- distatis(cubeOfCov$cubeOfCovariance, Distance = FALSE) # Project the vocabulary onto the factor space F4Voc <- projectVoc(BeersProjectiveMapping$CT.vocabulary, testDistatis$res4Splus$F)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.