gpa: Generalised Procrustes Analysis

Description Usage Arguments Details Value Author(s) References Examples

Description

Performs Generalised Procrustes Analysis (GPA) that takes into account missing values.

Usage

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GPA(df, tolerance=10^-10, nbiteration=200, scale=TRUE, 
    group, name.group = NULL, graph = TRUE, axes = c(1,2))

Arguments

df

a data frame with n rows (individuals) and p columns (quantitative varaibles)

tolerance

a threshold with respect to which the algorithm stops, i.e. when the difference between the GPA loss function at step n and n+1 is less than tolerance

nbiteration

the maximum number of iterations until the algorithm stops

scale

a boolean, if TRUE (which is the default value) scaling is required

group

a vector indicating the number of variables in each group

name.group

a vector indicating the name of the groups (the groups are successively named group.1, group.2 and so on, by default)

graph

boolean, if TRUE a graph is displayed

axes

a length 2 vector specifying the components to plot

Details

Performs a Generalised Procrustes Analysis (GPA) that takes into account missing values: some data frames of df may have non described or non evaluated rows, i.e. rows with missing values only.
The algorithm used here is the one developed by Commandeur.

Value

A list containing the following components:

RV

a matrix of RV coefficients between partial configurations

RVs

a matrix of standardized RV coefficients between partial configurations

simi

a matrix of Procrustes similarity indexes between partial configurations

scaling

a vector of isotropic scaling factors

dep

an array of initial partial configurations

consensus

a matrix of consensus configuration

Xfin

an array of partial configurations after transformations

correlations

correlation matrix between initial partial configurations and consensus dimensions

PANOVA

a list of "Procrustes Analysis of Variance" tables, per assesor (config), per product(objet), per dimension (dimension)

Author(s)

Elisabeth Morand

References

Commandeur, J.J.F (1991) Matching configurations.DSWO press, Leiden University.
Dijksterhuis, G. & Punter, P. (1990) Interpreting generalized procrustes analysis "Analysis of Variance" tables, Food Quality and Preference, 2, 255–265
Gower, J.C (1975) Generalized Procrustes analysis, Psychometrika, 40, 33–50
Kazi-Aoual, F., Hitier, S., Sabatier, R., Lebreton, J.-D., (1995) Refined approximations to permutations tests for multivariate inference. Computational Statistics and Data Analysis, 20, 643–656
Qannari, E.M., MacFie, H.J.H, Courcoux, P. (1999) Performance indices and isotropic scaling factors in sensory profiling, Food Quality and Preference, 10, 17–21

Examples

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## Not run: 
data(wine)
res.gpa <- GPA(wine[,-(1:2)], group=c(5,3,10,9,2),
    name.group=c("olf","vis","olfag","gust","ens"))

### If you want to construct the partial points for some individuals only
plotGPApartial (res.gpa)

## End(Not run)

Example output

There were 50 or more warnings (use warnings() to see the first 50)
     draw.partial
2EL         FALSE
1CHA        FALSE
1FON        FALSE
1VAU        FALSE
1DAM        FALSE
2BOU        FALSE
1BOI        FALSE
3EL         FALSE
DOM1        FALSE
1TUR        FALSE
4EL         FALSE
PER1        FALSE
2DAM        FALSE
1POY        FALSE
1ING        FALSE
1BEN        FALSE
2BEA        FALSE
1ROC        FALSE
2ING        FALSE
T1          FALSE
T2          FALSE

FactoMineR documentation built on Jan. 8, 2021, 2:18 a.m.