agricolae-xxx

agricolae tutorial (Version 1.3-5)

F elipe de Mendiburu

(1)

2021-06-05

1

Preface

The following document was developed to facilitate the use of agricolae package in R, it is understood

that the user knows the statistical methodology for the design and analysis of experiments and through

the use of the functions programmed in agricolae facilitate the generation of the field book experimental

soil consistency, late blight simulation and others.

1 Introduction

The package agricolae offers a broad functionality in the design of experiments, especially for experi-

ments in agriculture and improvements of plants, which can also be used for other purposes. It contains

the following designs: lattice, alpha, cyclic, balanced incomplete block designs, complete randomized

blocks, Latin, Graeco-Latin, augmented block designs, split plot and strip plot. It also has several

procedures of experimental data analysis, such as the comparisons of treatments of Waller-Duncan,

Bonferroni, Duncan, Student-Newman-Keuls, Scheffe, Ryan, Einot and Gabriel and Welsch multiple

range test or the classic LSD and Tukey; and non-parametric comparisons, such as Kruskal-Wallis,

Friedman, Durbin, Median and Waerden, stability analysis, and other procedures applied in genetics,

accessible to the current R session by the command:

Profesor Principal del Departamento Academico de Estad´ıstica e Inform´atica de la Facultad de Econom´ıa y Planifi-

caci´on. Universidad Nacional Agraria La Molina-PERU

1

For online help facilities or the details of a particular command (such as the function waller.test)

you can type:

1.2 Use in R

Since agricolae is a package of functions, these are operational when they are called directly from the

console of R and are integrated to all the base functions of R. The following orders are frequent:

For help:

1.3 Data set in agricolae

2

The package agricolae provides some complementary functions to the R program, specifically for the

management of the histogram and function hist.

2.1 Histogram

The histogram is constructed with the function graph.freq and is associated to other functions: poly-

gon.freq, table.freq, stat.freq. See Figures: 1, 2 and 3 for more details.

Example. Data generated in R . (students’ weight).

Frequency tables: Use table.freq, stat.freq and summary

The table.freq is equal to summary()

3

Figure 1: Absolute and relative frequency with polygon.

Limits class: Lower and Upper

Class point: Main

Frequency: Frequency

Percentage frequency: Percentage

Cumulative frequency: CF

Cumulative percentage frequency: CPF

You can extract information from a histogram such as class intervals intervals.freq, attract new in-

tervals with the sturges.freq function or to join classes with join.freq function. It is also possible to

reproduce the graph with the same creator graph.freq or function plot and overlay normal function with

normal.freq be it a histogram in absolute scale, relative or density . The following examples illustrates

these properties.

4

5