knitr::opts_chunk$set(echo = TRUE)

The **dad** package provides tools for analysing multi-group data.
Such data consist of variables observed on individuals, these individuals being organised into groups (or occasions).
Hence, there are three types of objects: groups, individuals and variables.

For the analysis of such data, a probability density function is associated to each group. Some methods dealing with these functions are implemented:

**Multidimensional scaling (MDS) of probability density functions**: function`fmdsd`

(continuous data) or`mdsdd`

(discrete data)

**Hierarchical cluster analysis (HCA) of probability density functions**:`fhclustd`

(continuous) or`hclustdd`

(discrete)**Discriminant analysis (DA) of probability density functions**:- Computation of the misclassification ratio using the one-leave-out method:
`fdiscd.misclass`

(continuous) or`discdd.misclass`

(discrete) - Assignment of groups of individuals, one group after another, for which the class is unknown:
`fdiscd.predict`

(continuous) or`discdd.predict`

(discrete)

In order to facilitate the work with these multi-group data, the **dad** package uses objects of class `"folder"`

or `"folderh"`

.
These objects are lists of data frames having particular formats.

`folder`

Such objects are lists of data frames which have the same column names. Each data frame matches with an occasion (a group of individuals).

An object of class `"folder"`

is created by the functions `folder`

or `as.folder`

(see their help in R).

**Example:**
Ten rosebushes $A$, $B$, $\dots$, $J$ were evaluated by 14 assessors, at three sessions, according to several descriptors including their shape `Sha`

, their foliage thickness `Den`

and their symmetry `Sym`

.

library(dad) data("roses") x <- roses[, c("Sha", "Den", "Sym", "rose")] head(x)

Coerce these data into an object of class `"folder"`

:

rosesf <- as.folder(x, groups = "rose") print(rosesf, max = 9)

`folderh`

Objects of class `"folderh"`

can be used to avoid redundancies in the data.

In the most useful case, such objects are hierarchical lists of two data frames `df1`

and `df2`

related by means of a key which describes the ā1 to Nā relationship between the data frames.

They are created by the function `folderh`

(see its help in R for the case of three data frames or more).

**Example:**
Data about 5 rosebushes (`roseflowers$variety`

). For each rosebush, measures on several flowers (`roseflowers$flower`

).

library(dad) data(roseflowers) df1 <- roseflowers$variety df2 <- roseflowers$flower

Build an object of class `"folderh"`

:

fh1 <- folderh(df1, "rose", df2) print(fh1)

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