Package 'statisR' reference manual

Title:	STATIS and STATIS DUAL Multivariate Methods
Description:	Provides tools for the integration and exploration of data tables measured on the same set of observational units. The package includes methods to assess similarities among tables, extract common patterns across variable blocks, and create visual summaries that highlight shared structures in multiblock data.
Authors:	Oldemar Rodriguez [aut, cre], Alejandro Vargas [ctb, prg]
Maintainer:	Oldemar Rodriguez <[email protected]>
License:	GPL (>= 2)
Version:	1.0.1
Built:	2026-06-01 10:13:23 UTC
Source:	https://github.com/cran/statisR

Sensory Evaluation Data from Expert 1

Description

This dataset contains the ratings provided by Expert 1 for six wine samples. Each wine is evaluated according to three sensory attributes commonly used in descriptive analysis: fruity, woody, and coffee. The dataset is typically used in STATIS and multitable analyses to illustrate how different experts evaluate the same set of products.

Usage

data(expert1)
data(expert1)

Format

A data frame with 6 rows (Wine1–Wine6) and 3 sensory attributes:

fruity: Intensity of fruity aromas.
woody: Intensity of woody/aged aromas.
coffee: Perceived coffee-like notes.

References

Abdi, H., & Valentin, D. (2007). The STATIS method. Encyclopedia of measurement and statistics, 955-962.

Examples

data(expert1)
expert1

data(expert1)
expert1

Sensory Evaluation Data from Expert 2

Description

This dataset contains the evaluations provided by Expert 2 for the same six wine samples. Unlike Expert 1, this expert uses four sensory descriptors: red_fruit, roasted, vanillin, and woody. The dataset demonstrates how experts may differ in terminology and profiling, and it is commonly used in STATIS, MFA, and other multitable comparison techniques.

Usage

data(expert2)
data(expert2)

Format

A data frame with 6 rows (Wine1–Wine6) and 4 sensory attributes:

red_fruit: Intensity of red fruit aromas.
roasted: Intensity of roasted or toasted notes.
vanillin: Perceived vanilla-related notes.
woody: Intensity of woody/aged aromas.

References

Abdi, H., & Valentin, D. (2007). The STATIS method. Encyclopedia of measurement and statistics, 955-962.

Examples

data(expert2)
expert2

data(expert2)
expert2

Sensory Evaluation Data from Expert 3

Description

This dataset contains the ratings given by Expert 3 for the same set of six wine samples. This expert evaluates wines using three sensory attributes: fruity, butter, and woody. The dataset is often used in multivariate and STATIS examples to highlight both agreement and divergence across panels of experts.

Usage

data(expert3)
data(expert3)

Format

A data frame with 6 rows (Wine1–Wine6) and 3 sensory attributes:

fruity: Intensity of fruity aromas.
butter: Presence of buttery or lactic notes.
woody: Intensity of woody/aged aromas.

References

Abdi, H., & Valentin, D. (2007). The STATIS method. Encyclopedia of measurement and statistics, 955-962.

Examples

data(expert3)
expert3

data(expert3)
expert3

Plot a Correlation Circle (Unit Circle)

Description

This function generates a correlation circle plot from two-dimensional coordinates, commonly used in principal component analysis (PCA) or other multivariate methods.

Usage

## S3 method for class 'statis.circle'
plot(points, inertia = 100, labels = NULL, title = "")
## S3 method for class 'statis.circle'
plot(points, inertia = 100, labels = NULL, title = "")

Arguments

points

A matrix or data frame with two numeric columns (x, y) representing the coordinates of the vectors.

inertia

A number between 0 and 100 representing the percentage of explained inertia. It is displayed in the title.

labels

A character vector with labels for the points (optional). If not specified, labels are left blank.

title

Optional text used as the main title of the plot.

Details

Arrows are drawn from the origin to each point specified in points. A reference circle with radius 1 is displayed. You can also show the percentage of explained inertia and point labels.

The inertia argument is flexible and can be passed as the second or third parameter if argument names are omitted.

Value

A ggplot object with the generated plot.

Examples

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or row just set the parameters in the statis function.

res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# Circle of correlations of all the tables
inter <- res$circle.inter
plot.statis.circle(inter$points, inter$inertia, inter$labels, inter$title)

# Circle of correlations of all variables evolution
intra <- res$circle.intra
plot.statis.circle(intra$points, intra$inertia, intra$labels, intra$title)

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or row just set the parameters in the statis function.

res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# Circle of correlations of all the tables
inter <- res$circle.inter
plot.statis.circle(inter$points, inter$inertia, inter$labels, inter$title)

# Circle of correlations of all variables evolution
intra <- res$circle.intra
plot.statis.circle(intra$points, intra$inertia, intra$labels, intra$title)

Bivariate PCA-style Scatter Plot

Description

This function generates a 2D scatter plot with support for multiple groups, labels, arrows from the origin, reference circles, cross axes, and full style customization using ggplot2.

Usage

## S3 method for class 'statis.dual.circle'
plot(
  points.list,
  style.points = list(list(size = 3)),
  style.circle = list(),
  radius.circle = 1,
  labels = "auto",
  labels.style = NULL,
  draw.labels = TRUE,
  vars.direction = NULL,
  style.vars = list(),
  radius.vars = c(0.5, 1),
  join.dots = FALSE,
  style.join = list(),
  base.colors = .base.colors,
  axes = TRUE,
  frame = TRUE,
  hide.ticks = TRUE,
  proportion = 1,
  xlim = NULL,
  ylim = NULL,
  axes.xy = TRUE,
  style.axes.xy = list(linewidth = 0.35, linetype = "dashed", color = "gray40"),
  arrows.points = TRUE,
  factor.arrow = 0.95,
  style.arrows = list(color = "red", linewidth = 0.6, arrow = grid::arrow(length =
    grid::unit(0.2, "cm")))
)
## S3 method for class 'statis.dual.circle'
plot(
  points.list,
  style.points = list(list(size = 3)),
  style.circle = list(),
  radius.circle = 1,
  labels = "auto",
  labels.style = NULL,
  draw.labels = TRUE,
  vars.direction = NULL,
  style.vars = list(),
  radius.vars = c(0.5, 1),
  join.dots = FALSE,
  style.join = list(),
  base.colors = .base.colors,
  axes = TRUE,
  frame = TRUE,
  hide.ticks = TRUE,
  proportion = 1,
  xlim = NULL,
  ylim = NULL,
  axes.xy = TRUE,
  style.axes.xy = list(linewidth = 0.35, linetype = "dashed", color = "gray40"),
  arrows.points = TRUE,
  factor.arrow = 0.95,
  style.arrows = list(color = "red", linewidth = 0.6, arrow = grid::arrow(length =
    grid::unit(0.2, "cm")))
)

Arguments

points.list

List of numeric objects (matrices, data.frames, or lists of vectors), each representing a group of 2D points.

style.points

List of ggplot2 styles applied per group. If NULL, geom_point() is used by default.

style.circle

List of styles for the reference circle (passed to ggforce::geom_circle).

radius.circle

Radius (or vector of radii) to draw circles centered at the origin. Default value 1, if 0, no circles are drawn.

labels

"auto" generates numeric labels by group, or it can be a vector/list of custom labels.

labels.style

List of styles for the labels, passed to ggrepel::geom_text_repel.

draw.labels

Logical. If TRUE, labels are drawn on the points.

vars.direction

Directions of projected variables.

style.vars

Style for projected variables.

radius.vars

Radii used to scale variable arrows.

join.dots

Logical or list. If TRUE, connects points by group. If a list, connects points as manually defined.

style.join

List of styles for connecting points (passed to geom_path()).

base.colors

Vector of base colors used for the groups.

axes

Logical. If FALSE, all axis elements are removed.

frame

Logical. Not directly used; may be reserved for future use.

hide.ticks

Logical. If TRUE, hides axis ticks and text.

proportion

Fixed aspect ratio of the plot (to avoid distortion).

xlim

X-axis limits.

ylim

Y-axis limits.

axes.xy

Logical. If TRUE, draws cross axes (X = 0, Y = 0) with defined style.

style.axes.xy

List of styles for the XY cross axes (e.g., linetype, color, etc.).

arrows.points

Logical. If TRUE, draws arrows from the origin to each point.

factor.arrow

Factor to shorten the arrows.

style.arrows

List of styles for the arrows.

Value

A ggplot object with the generated plot.

Examples

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels <- c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# Interstructure
t <- ggplot2::ggtitle("Interstructure")
plot.statis.dual.circle(points.list = list(res$interstructure), labels = res$labels.tables) + t

# Circle of correlations (all variables)
t <- ggplot2::ggtitle("Correlation (all variables)")
plot.statis.dual.circle(list(res$supervariables), labels = row.names(res$supervariables)) + t

# Circle of correlations (variables selected)
selected.variables <- c("Ph", "Temp", "DBO", "ST", "PO4", "NO3", "POD", "Cal")
superv.sel.df <- select.super.variables(res$supervariables, res$vars.names, selected.variables)

t <- ggplot2::ggtitle("Correlation (selected variables)")
plot.statis.dual.circle(list(superv.sel.df), labels = row.names(superv.sel.df)) + t

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels <- c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# Interstructure
t <- ggplot2::ggtitle("Interstructure")
plot.statis.dual.circle(points.list = list(res$interstructure), labels = res$labels.tables) + t

# Circle of correlations (all variables)
t <- ggplot2::ggtitle("Correlation (all variables)")
plot.statis.dual.circle(list(res$supervariables), labels = row.names(res$supervariables)) + t

# Circle of correlations (variables selected)
selected.variables <- c("Ph", "Temp", "DBO", "ST", "PO4", "NO3", "POD", "Cal")
superv.sel.df <- select.super.variables(res$supervariables, res$vars.names, selected.variables)

t <- ggplot2::ggtitle("Correlation (selected variables)")
plot.statis.dual.circle(list(superv.sel.df), labels = row.names(superv.sel.df)) + t

Plot Variable Trajectories in STATIS DUAL

Description

Visualizes the evolution of one or more variables across the different tables in a STATIS DUAL analysis. Each trajectory represents the sequence of positions of a variable in the compromise space.

Usage

## S3 method for class 'statis.dual.trajectories'
plot(
  vars,
  trajectories,
  labels.tables,
  .range = NULL,
  style.line = list(linetype = 2, linewidth = 0.5, color = "orange"),
  point.size = 3,
  base.colors = c("red", "blue", "brown", "darkgreen", "purple", "orange", "cyan4",
    "gold3", "black")
)
## S3 method for class 'statis.dual.trajectories'
plot(
  vars,
  trajectories,
  labels.tables,
  .range = NULL,
  style.line = list(linetype = 2, linewidth = 0.5, color = "orange"),
  point.size = 3,
  base.colors = c("red", "blue", "brown", "darkgreen", "purple", "orange", "cyan4",
    "gold3", "black")
)

Arguments

vars

Vector of variable names to plot (must match the names in trajectories).

trajectories

List generated by statis.dual()$trajectories, where each element is a K x 2 matrix.

labels.tables

Vector of length K with the names or labels of the tables.

.range

List with axis limits: list(x = c(xmin, xmax), y = c(ymin, ymax)). If NULL, limits are computed automatically.

style.line

List with line style for the trajectories.

point.size

Size of the points at each position along the trajectory.

base.colors

Vector of base colors to distinguish the variables.

Value

A ggplot object showing the trajectories of the selected variables.

Examples

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels = c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# If you want to select some variables
vars.A <- c("Ph","ST","NO3")
t <- ggplot2::ggtitle(sprintf("Trayectorias (%s)", paste(vars.A, collapse = ", ")))

plot.statis.dual.trajectories(vars.A, res$trajectories, res$labels.tables) + t

# If you want to select an specific variable
vars.1 <- "Temp"
t <- ggplot2::ggtitle(sprintf("Trajectory (%s)", vars.1))

plot.statis.dual.trajectories(vars.1, res$trajectories, res$labels.tables) + t

# All variables
t <- ggplot2::ggtitle("Trajectories (all variables)")
plot.statis.dual.trajectories(res$vars.names, res$trajectories, res$labels.tables) + t

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels = c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# If you want to select some variables
vars.A <- c("Ph","ST","NO3")
t <- ggplot2::ggtitle(sprintf("Trayectorias (%s)", paste(vars.A, collapse = ", ")))

plot.statis.dual.trajectories(vars.A, res$trajectories, res$labels.tables) + t

# If you want to select an specific variable
vars.1 <- "Temp"
t <- ggplot2::ggtitle(sprintf("Trajectory (%s)", vars.1))

plot.statis.dual.trajectories(vars.1, res$trajectories, res$labels.tables) + t

# All variables
t <- ggplot2::ggtitle("Trajectories (all variables)")
plot.statis.dual.trajectories(res$vars.names, res$trajectories, res$labels.tables) + t

Plot a Plane of Observations or 2D Projections

Description

This function generates a two-dimensional scatter plot with centered axes, useful for representing the results of multivariate analyses.

Usage

## S3 method for class 'statis.plane'
plot(points, inertia = 100, labels = NULL, title = "")
## S3 method for class 'statis.plane'
plot(points, inertia = 100, labels = NULL, title = "")

Arguments

points

A matrix, data frame, or a length-2 vector with coordinates (x, y). Must have exactly two columns.

inertia

A number between 0 and 100 indicating the percentage of explained inertia (optional, defaults to 100).

labels

A character vector with labels for the points (optional). Must match the number of rows in points.

title

A text string to be used as the main title of the plot (optional).

Details

The plot includes points, optional labels, Cartesian axes (centered at 0), and a title indicating the percentage of explained inertia.

Value

A ggplot object with the generated plot.

Examples

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or row just set the parameters in the statis function.

res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# Main Plane of Average Individuals
individuals <- res$plane.individuals
plot.statis.plane(individuals$points, individuals$inertia, individuals$labels, individuals$title)

# Main Plane of the Evolution of individuals
evolution <- res$plane.evolution
plot.statis.plane(evolution$points, evolution$inertia, evolution$labels, evolution$title)

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or row just set the parameters in the statis function.

res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# Main Plane of Average Individuals
individuals <- res$plane.individuals
plot.statis.plane(individuals$points, individuals$inertia, individuals$labels, individuals$title)

# Main Plane of the Evolution of individuals
evolution <- res$plane.evolution
plot.statis.plane(evolution$points, evolution$inertia, evolution$labels, evolution$title)

Select and prepare a subset of variables from a supervision matrix

Description

This function selects a predefined subset of variables (ETCal) from a supervision matrix (superv), checks dimension consistency, verifies missing variables, and constructs a clean data frame containing the first two coordinates typically used for PCA or STATIS correlation plots.

Usage

select.super.variables(superv, ET, ETCal)
select.super.variables(superv, ET, ETCal)

Arguments

superv

A numeric matrix or data frame where each row corresponds to a variable and columns represent coordinates (res$supervariables).

ET

A character vector containing the full list of expected variable names (res$vars.names).

ETCal

A character vector containing the subset of variables to be selected.

Details

The function performs the following steps:

Checks that the number of rows in superv matches the length of ET.
Assigns the row names of superv using ET.
Identifies whether any variables in ETCal are missing in superv; missing variables trigger a warning.
Creates an ordered list of valid variables (ETCal_ok) based on their presence in superv.
Extracts the corresponding rows from superv and constructs a data frame with columns x and y.

Value

A data frame with two columns:

x: first coordinate (e.g., PC1)
y: second coordinate (e.g., PC2)

Row names correspond to the selected variables defined in ETCal.

Examples

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels <- c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

ETCal <- c("Ph","Temp","DBO","ST","PO4","NO3","POD","Cal")

df_selected <- select.super.variables(res$supervariables, res$vars.names, ETCal)

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels <- c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

ETCal <- c("Ph","Temp","DBO","ST","PO4","NO3","POD","Cal")

df_selected <- select.super.variables(res$supervariables, res$vars.names, ETCal)

STATIS Method

Description

Applies the STATIS method to a set of matrices (data tables) with the same rows. Is a multivariate analysis technique that allows studying the common structure and the evolution of individuals and variables across multiple tables.

Usage

statis(
  matrices,
  selected.tables = NULL,
  selected.rows = NULL,
  table.labels = NULL
)
statis(
  matrices,
  selected.tables = NULL,
  selected.rows = NULL,
  table.labels = NULL
)

Arguments

matrices

List of numeric matrices (at least 2), all with the same number of rows (individuals).

selected.tables

Select a subset of tables. If NULL, all tables are included.

selected.rows

Select a subset of rows. If NULL, all rows are included.

table.labels

Optional vector with names for the tables. It must have the same length as the number of tables.

Value

A list with the following elements:

n

Number of rows (individuals).

r

Number of tables.

p

Vector with the number of columns per table.

S

List of centered matrices.

W

List of proximity matrices.

X

Matrix of interstructure (vectorization of W).

acp.inter

PCA results of the interstructure: eigenvalues, eigenvectors, components, correlations.

XT

Weighted average of the matrices.

acp.intra

PCA results of the average: eigenvalues, eigenvectors, components, correlations.

IND

Concatenated matrix with all W stacked (individual evolution).

Omega

Projection of individual evolution onto the principal components.

circle.inter

Data to plot the correlation circle between tables.

circle.intra

Data to plot the variable evolution circle.

plane.individuals

Data to plot the average individuals plane.

plane.evolution

Data to plot the evolution of the individuals.

Examples

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or tables
res <- statis(list(expert1, expert2, expert3), selected.tables = c(1, 3), table.labels = labels)

# If you want to select an specific row or rows
res <- statis(list(expert1, expert2, expert3), selected.rows = c(1, 5), table.labels = labels)

# If you want to select some tables and rows at the same time
res <- statis(list(expert1,expert2,expert3), selected.tables=c(1, 3), selected.rows=c(1, 4), labels)

# All tables and rows selected
res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# How to use res
inter <- res$circle.inter
plot.statis.circle(inter$points, inter$inertia, inter$labels, inter$title)

evolution <- res$plane.evolution
plot.statis.plane(evolution$points, evolution$inertia, evolution$labels, evolution$title)

data(expert1, expert2, expert3)

labels <- c("Expert 1", "Expert 2", "Expert 3")

# If you want to select an specific table or tables
res <- statis(list(expert1, expert2, expert3), selected.tables = c(1, 3), table.labels = labels)

# If you want to select an specific row or rows
res <- statis(list(expert1, expert2, expert3), selected.rows = c(1, 5), table.labels = labels)

# If you want to select some tables and rows at the same time
res <- statis(list(expert1,expert2,expert3), selected.tables=c(1, 3), selected.rows=c(1, 4), labels)

# All tables and rows selected
res <- statis(list(expert1, expert2, expert3), table.labels = labels)

# How to use res
inter <- res$circle.inter
plot.statis.circle(inter$points, inter$inertia, inter$labels, inter$title)

evolution <- res$plane.evolution
plot.statis.plane(evolution$points, evolution$inertia, evolution$labels, evolution$title)

Physicochemical Variables Dataset for STATIS Analysis

Description

This dataset belongs to a project called “Development and application of effective, low-cost methods for the biological monitoring of Costa Rican rivers” by the National University, and contains measurements of various physicochemical variables collected across several sampling sites. It is intended to be used in the examples and demonstrations of the main functions of the package, particularly those related to multivariate analysis and STATIS methodology.

Usage

data(STATIS_TABLE1)
data(STATIS_TABLE1)

Format

A data frame with 14 columns and multiple rows (one per sampling site):

NIT: Total nitrogen level.
FOS: Phosphorus level.
CAL: Calcium level.
STO: Sodium level.
PH: pH measurement.
MN: Manganese concentration.
ZN: Zinc concentration.
SS: Suspended solids.
ALC: Alkalinity.
CL: Chlorine level.
CAU: Exchangeable calcium or equivalent measurement.
DBO: Biological oxygen demand.
POR: Porosity or related percentage.

Examples

data(STATIS_TABLE1)
head(STATIS_TABLE1)

data(STATIS_TABLE1)
head(STATIS_TABLE1)

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

data(STATIS_TABLE1)
data(STATIS_TABLE1)

Format

A data frame with 14 columns and multiple rows (one per sampling site):

NIT: Total nitrogen level.
FOS: Phosphorus level.
CAL: Calcium level.
STO: Sodium level.
PH: pH measurement.
MN: Manganese concentration.
ZN: Zinc concentration.
SS: Suspended solids.
ALC: Alkalinity.
CL: Chlorine level.
CAU: Exchangeable calcium or equivalent measurement.
DBO: Biological oxygen demand.
POR: Porosity or related percentage.

Examples

data(STATIS_TABLE2)
head(STATIS_TABLE2)

data(STATIS_TABLE2)
head(STATIS_TABLE2)

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

data(STATIS_TABLE3)
data(STATIS_TABLE3)

Format

A data frame with 14 columns and multiple rows (one per sampling site):

NIT: Total nitrogen level.
FOS: Phosphorus level.
CAL: Calcium level.
STO: Sodium level.
PH: pH measurement.
MN: Manganese concentration.
ZN: Zinc concentration.
SS: Suspended solids.
ALC: Alkalinity.
CL: Chlorine level.
CAU: Exchangeable calcium or equivalent measurement.
DBO: Biological oxygen demand.
POR: Porosity or related percentage.

Examples

data(STATIS_TABLE3)
head(STATIS_TABLE3)

data(STATIS_TABLE3)
head(STATIS_TABLE3)

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

data(STATIS_TABLE4)
data(STATIS_TABLE4)

Format

A data frame with 14 columns and multiple rows (one per sampling site):

NIT: Total nitrogen level.
FOS: Phosphorus level.
CAL: Calcium level.
STO: Sodium level.
PH: pH measurement.
MN: Manganese concentration.
ZN: Zinc concentration.
SS: Suspended solids.
ALC: Alkalinity.
CL: Chlorine level.
CAU: Exchangeable calcium or equivalent measurement.
DBO: Biological oxygen demand.
POR: Porosity or related percentage.

Examples

data(STATIS_TABLE4)
head(STATIS_TABLE4)

data(STATIS_TABLE4)
head(STATIS_TABLE4)

STATIS DUAL Analysis

Description

Implementation of the STATIS DUAL method for the joint analysis of multiple tables that share the same variables. This approach allows evaluating the common structure between tables (interstructure), building a compromise (weighted average of structures), and analyzing the trajectories of variables across the tables.

Usage

statis.dual(tables, labels.tables = NULL)
statis.dual(tables, labels.tables = NULL)

Arguments

tables

A list of matrices or data frames with the same columns (variables). Each element represents a "table".

labels.tables

A vector of length equal to the number of tables, used to name the tables in the results. If NULL, labels like ("T1", "T2", ...) are auto-generated.

Details

The STATIS DUAL method allows:

Evaluating structural coherence across multiple tables using the interstructure
Constructing a representative compromise of the set of tables
Analyzing the behavior of the variables across the set (trajectories)

Internally, the tables are centered and normalized considering uniform observation weights. The R matrices capture the internal structure of each table. The interstructure is based on scalar products between these matrices.

Value

A list with the following elements:

labels.tables

Vector with the table labels

interstructure

K x 2 matrix with the coordinates of the tables in the interstructure space

supervariables

p x 2 matrix with the coordinates of the variables in the compromise

trajectories

List of p K x 2 matrices, one per variable, showing its trajectory across the tables

vars.names

Names of the variables (common columns)

S

Interstructure similarity matrix

R

List of R matrices for each table

Comp

Compromise matrix (weighted combination of R matrices)

eigenvalues.compromise

Eigenvalues of the compromise (inertia per axis)

eigenvectors.compromise

Eigenvectors of the compromise

beta.weights

Weights of each table in the construction of the compromise

Examples

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels = c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# How to use res
t <- ggplot2::ggtitle("Correlation (all variables)")
plot.statis.dual.circle(list(res$supervariables), labels = row.names(res$supervariables)) + t

t <- ggplot2::ggtitle("Trajectories (all variables)")
plot.statis.dual.trajectories(res$vars.names, res$trajectories, res$labels.tables) + t

data(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98)
labels = c("95","96","97","98")

res <- statis.dual(list(Tuis5_95, Tuis5_96, Tuis5_97, Tuis5_98), labels.tables = labels)

# How to use res
t <- ggplot2::ggtitle("Correlation (all variables)")
plot.statis.dual.circle(list(res$supervariables), labels = row.names(res$supervariables)) + t

t <- ggplot2::ggtitle("Trajectories (all variables)")
plot.statis.dual.trajectories(res$vars.names, res$trajectories, res$labels.tables) + t

STATIS and STATIS Dual Multivariate Methods

Description

STATIS is a multivariate technique developed in France in the 1970s by L'Hermier des Plantes and Escoufier, designed to analyze multiple data tables. This package provides an implementation of methods for the joint analysis of multiple data tables sharing the same set of observations. Includes functions for performing inter-structure and intra-structure analyses, as well as graphical representations of compromise structures and the shared variability across different groups of variables.

Details

Package:	statisR
Type:	Package
Version:	1.0.1
Date:	2025-11-28
License:	GPL (>=2)

Author(s)

Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas [email protected]

References

Abdi, H., & Valentin, D. (2007). The STATIS method. Encyclopedia of measurement and statistics, 955-962.

González, J., & Rodrıguez, O. (1995). Algoritmo e implementación del método Statis. In IX Simposio Métodos Matemáticos Aplicados a las Ciencias. UCR-ITCR, Turrialba.

Elizondo, W. C., & Varela, J. G. (1998). Stadis dual: software y análisis de datos reales. Revista de Matemática: Teoría y Aplicaciones, 5(2), 149-162.

Abdi, H. (2007). RV coefficient and congruence coefficient. Encyclopedia of measurement and statistics, 849(853), 92.

Abdi, H. (2007). Singular value decomposition (SVD) and generalized singular value decomposition. Encyclopedia of measurement and statistics, 907(912), 44.

Xiang, G. (2007). Fast algorithms for computing statistics under interval uncertainty, with applications to computer science and to electrical and computer engineering. The University of Texas at El Paso.

McHale, D., & Lavit, C. (1990). Analyse Conjointe de Tableaux Quantitatifs. Biometrics, 46(2), 542.

Physicochemical Quality Data (Tuis5_95)

Description

This dataset contains physicochemical measurements collected from a Sugarcane Fertilizer experiment in Costa Rica. The values represent indicators measured during a monitoring campaign. The dataset is useful for illustrating multivariate methods, STATIS analyses, or environmental data exploration workflows.

Usage

data(Tuis5_95)
data(Tuis5_95)

Format

A data frame with 10 observations and 19 variables:

Ph: pH value of the sample.
Temp: Temperature (°C).
Na: Sodium concentration.
Ka: Potassium concentration.
Ca: Calcium concentration.
Mg: Magnesium concentration.
Si02: Silica concentration.
OD: Dissolved oxygen.
DBO: Biochemical oxygen demand (BOD).
SD: Dissolved solids.
ST: Total solids.
PO4: Phosphate concentration.
Cl: Chloride concentration.
NO3: Nitrate concentration.
SO45: Sulfate concentration.
HC03: Bicarbonate concentration.
DT: Total hardness or related measurement.
POD: Dissolved oxygen percentage or related measure.
Cal: Calcium-related parameter (likely residual hardness).

Examples

data(Tuis5_95)
head(Tuis5_95)

data(Tuis5_95)
head(Tuis5_95)

Physicochemical Quality Data (Tuis5_96)

Description

Usage

data(Tuis5_96)
data(Tuis5_96)

Format

A data frame with 12 observations and 19 variables:

Ph: pH value of the water sample.
Temp: Temperature (°C).
Na: Sodium concentration.
Ka: Potassium concentration.
Ca: Calcium concentration.
Mg: Magnesium concentration.
Si02: Silica concentration.
OD: Dissolved oxygen.
DBO: Biochemical oxygen demand (BOD).
SD: Dissolved solids.
ST: Total solids.
PO4: Phosphate concentration.
Cl: Chloride concentration.
NO3: Nitrate concentration.
SO45: Sulfate concentration.
HC03: Bicarbonate concentration.
DT: Total hardness or related parameter.
POD: Dissolved oxygen percentage or related measurement.
Cal: Calcium-related parameter (e.g., residual hardness).

Examples

data(Tuis5_96)
head(Tuis5_96)

data(Tuis5_96)
head(Tuis5_96)

Physicochemical Quality Data (Tuis5_97)

Description

Usage

data(Tuis5_97)
data(Tuis5_97)

Format

A data frame with 12 observations and 19 variables:

Ph: pH value of the water sample.
Temp: Temperature (°C).
Na: Sodium concentration.
Ka: Potassium concentration.
Ca: Calcium concentration.
Mg: Magnesium concentration.
Si02: Silica concentration.
OD: Dissolved oxygen.
DBO: Biochemical oxygen demand (BOD).
SD: Dissolved solids.
ST: Total solids.
PO4: Phosphate concentration.
Cl: Chloride concentration.
NO3: Nitrate concentration.
SO45: Sulfate concentration.
HC03: Bicarbonate concentration.
DT: Total hardness or related parameter.
POD: Dissolved oxygen percentage or related measure.
Cal: Calcium-related parameter (e.g., residual hardness).

Examples

data(Tuis5_97)
head(Tuis5_97)

data(Tuis5_97)
head(Tuis5_97)

Physicochemical Quality Data (Tuis5_98)

Description

Usage

data(Tuis5_98)
data(Tuis5_98)

Format

A data frame with 12 observations and 19 variables:

Ph: pH value of the water sample.
Temp: Temperature (°C).
Na: Sodium concentration.
Ka: Potassium concentration.
Ca: Calcium concentration.
Mg: Magnesium concentration.
Si02: Silica concentration.
OD: Dissolved oxygen.
DBO: Biochemical oxygen demand (BOD).
SD: Dissolved solids.
ST: Total solids.
PO4: Phosphate concentration.
Cl: Chloride concentration.
NO3: Nitrate concentration.
SO45: Sulfate concentration.
HC03: Bicarbonate concentration.
DT: Total hardness or related parameter.
POD: Dissolved oxygen percentage or related measurement.
Cal: Calcium-related parameter (e.g., residual hardness).

Examples

data(Tuis5_98)
head(Tuis5_98)

data(Tuis5_98)
head(Tuis5_98)

Package 'statisR'

Help Index

Sensory Evaluation Data from Expert 1

Description

Usage

Format

References

Examples

Sensory Evaluation Data from Expert 2

Description

Usage

Format

References

Examples

Sensory Evaluation Data from Expert 3

Description

Usage

Format

References

Examples

Plot a Correlation Circle (Unit Circle)

Description

Usage

Arguments

Details

Value

See Also

Examples

Bivariate PCA-style Scatter Plot

Description

Usage

Arguments

Value

See Also

Examples

Plot Variable Trajectories in STATIS DUAL

Description

Usage

Arguments

Value

See Also

Examples

Plot a Plane of Observations or 2D Projections

Description

Usage

Arguments

Details

Value

See Also

Examples

Select and prepare a subset of variables from a supervision matrix

Description

Usage

Arguments

Details

Value

Examples

STATIS Method

Description

Usage

Arguments

Value

See Also

Examples

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

Format

Examples

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

Format

Examples

Physicochemical Variables Dataset for STATIS Analysis

Description

Usage

Format

Examples

Physicochemical Variables Dataset for STATIS Analysis