View source: R/dstatis.inter.R
| dstatis.inter | R Documentation |
Performs the first stage (interstructure) of the dual STATIS method in order to describe a data folder, consisting of T groups of individuals on which are observed p variables. It returns an object of class dstatis.
dstatis.inter(xf, normed = TRUE, centered = TRUE, data.scaled = FALSE, nb.factors = 3,
nb.values = 10, sub.title = "", plot.eigen = TRUE, plot.score = FALSE,
nscore = 1:3, group.name = "group", filename = NULL)
xf |
object of class |
normed |
logical. If |
centered |
logical. If |
data.scaled |
logical. If |
nb.factors |
numeric. Number of returned principal scores (default |
nb.values |
numerical. Number of returned eigenvalues (default |
sub.title |
string. If provided, the subtitle for the graphs. |
plot.eigen |
logical. If |
plot.score |
logical. If |
nscore |
numeric vector. If |
group.name |
string. Name of the grouping variable. Default: |
filename |
string. Name of the file in which the results are saved. By default ( |
The covariance matrices (if data.scale is FALSE) or correlation matrices (if TRUE) per group are computed. The matrix W of the scalar products between these covariance matrices is then computed.
To perform the STATIS method, see the function DSTATIS
of the multigroup package.
Returns an object of class dstatis, that is a list including:
inertia |
data frame of the eigenvalues and percentages of inertia. |
contributions |
data frame of the contributions to the first |
qualities |
data frame of the qualities on the first |
scores |
data frame of the first |
norm |
vector of the |
means |
list of the means. |
variances |
list of the covariance matrices. |
correlations |
list of the correlation matrices. |
skewness |
list of the skewness coefficients. |
kurtosis |
list of the kurtosis coefficients. |
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard
Lavit, C., Escoufier, Y., Sabatier, R., Traissac, P. (1994). The ACT (STATIS method). Computational Statistics & Data Analysis, 18 (1994), 97-119.
print.dstatis, plot.dstatis, interpret.dstatis.
data(roses)
rosesf <- as.folder(roses[,c("Sha","Den","Sym","rose")])
# Dual STATIS on the covariance matrices
result1 <- dstatis.inter(rosesf, data.scaled = FALSE, group.name = "rose")
print(result1)
plot(result1)
# Dual STATIS on the correlation matrices
result2 <- dstatis.inter(rosesf, data.scaled = FALSE, group.name = "rose")
print(result2)
plot(result2)
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