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AgroR

Package: AgroR

Type: Package

Title: Experimental Statistics and Graphics for Agricultural Sciences

Version: 1.3.6

Date: 2023-26-12

Authors:

  • Gabriel Danilo Shimizu
  • Rodrigo Yudi Palhaci Marubayashi
  • Leandro Simoes Azeredo Goncalves

Maintainer: Gabriel Danilo Shimizu shimizu@uel.br

Description: Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi: 10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).

Encoding: UTF-8

RoxygenNote: 7.1.1

Imports: ggplot2, nortest, lme4, crayon, lmtest, emmeans, multcomp, ggrepel, MASS, cowplot, multcompView, RColorBrewer, drc, dunn.test, gtools

Suggests: DT, knitr, rmarkdown, roxygen2

Depends: R (>= 3.6.0)

License: GPL (>= 2)

Installation

# Install release version from CRAN
install.packages("AgroR")

# Install development version from GitHub
devtools::install_github("https://github.com/AgronomiaR/AgroR.git")

References

Data set

  • aristolochia: Germination of seeds of Aristolochia sp. as a function of temperature.
  • bean: Bean data
  • cloro: Sodium dichloroisocyanurate in soybean
  • corn: Corn data
  • covercrops: Covercrops data
  • emerg: Emergence of passion fruit seeds over time .
  • enxofre: Sulfur data
  • laranja: Orange plants under different rootstocks
  • mirtilo: Cutting blueberry data
  • orchard: Orchard data
  • passiflora: Substrate data in the production of passion fruit seedlings
  • pepper: Pepper data
  • phao: Osmocote in Phalaenopsis sp.
  • pomegranate: Pomegranate data
  • porco: Pig development and production
  • sensorial: Sensorial data
  • simulate1: Simulated data dict
  • simulate2: Simulated data dbct
  • simulate3: Simulated data dqlt
  • soybean: Soybean data
  • tomate: Tomato data
  • weather: Weather data

Descritive analysis

  • desc: Descriptive analysis
  • desc2fat: Descriptive analysis (Two factors)
  • desc3fat: Descriptive analysis (Three factors)
  • dispvar: Boxplot with standardized data
  • tabledesc: Table descritive analysis

Analysis function

t test to compare means with a reference value

  • tonetest: t test to compare means with a reference value

Analysis for testing of two independent or dependent samples by parametric or non-parametric method

  • test_two: Test for two samples

Analysis of simple experiments

  • DIC: Completely randomized design
  • DBC: Randomized block design
  • DQL: Latin square design

Analysis of simple experiments with an additional treatment for quantitative factor

  • dic.ad: Completely randomized design with an additional treatment for quantitative factor
  • dbc.ad: Randomized block design with an additional treatment for quantitative factor

Analysis of simple experiments in DIC and DBC by generalized linear model (Binomial or Poisson)

  • DIC.glm: Completely randomized design by glm
  • DBC.glm: Randomized block design by glm

Analysis of experiments in DIC, DBC or DQL with multiple assessments over time or disregarding the effect of another factor

  • DICT: Completely randomized design evaluated over time
  • DBCT: Randomized block design evaluated over time
  • DQLT: Latin square design evaluated over time

Analysis of groups of experiments in DIC and DBC

  • conjdbc: Joint analysis of experiments in randomized block design
  • conjdic: Joint analysis of experiments in completely randomized design

Analysis of groups of experiments in FAT2DBC

  • conjfat2dbc: Joint analysis of experiments in randomized block design in double factorial

Analysis of experiments in double factorial design in DIC and DBC

  • FAT2DIC: DIC experiments in double factorial
  • FAT2DBC: DBC experiments in double factorial

Analysis of double factorial design experiments in DIC or DBC with an additional treatment

  • FAT2DIC.ad: DIC experiment in double factorial design with an additional treatment
  • FAT2DBC.ad: DBC experiment in double factorial design with an additional treatment

Analysis of DIC or DBC experiments in a factorial scheme with three factors

  • FAT3DIC: DIC experiments in triple factorial
  • FAT3DBC: DBC experiments in triple factorial

Analysis of triple factorial design experiments in DIC or DBC with an additional treatment

  • FAT3DIC.ad: DIC experiment in double factorial design with an additional treatment
  • FAT3DBC.ad: DBC experiment in double factorial design with an additional treatment

Split-plot scheme in DIC or DBC

  • PSUBDBC: DBC experiments in split-plot
  • PSUBDIC: DIC experiments in split-plot

Splitsplitplot parcels scheme in DBC

  • PSUBSUBDBC: DBC experiments in split-split-plot

Plot subdivided into randomized blocks with a subplot in a double factorial scheme

  • PSUBFAT2DBC: Plot subdivided into randomized blocks with a subplot in a double factorial scheme

Dunnett's Test for Comparison of Control vs. Treatments

  • dunnett: Dunnett test

Dunn's non-parametric test

  • dunn: Dunn test

Logistic regression 3 or 4 parameters

  • logistic: Logistic regression

Polynomial Regression to the Third Degree

  • polynomial: Linear regression graph
  • polynomial2: Linear regression graph in double factorial
  • polynomial2_color: Linear regression graph in double factorial with color graph

Principal component analysis

  • PCA_function: Principal components analysis

Graphs

  • barfacet: Bar graph for one factor with facets
  • bargraph_onefactor: Group DIC, DBC and DQL functions column charts
  • bargraph_twofactor: Group FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC functions column charts
  • barplot_positive: Positive barplot
  • bar_dunnett: Barplot for Dunnett test
  • bar_graph: Bar graph for one factor
  • bar_graph2: Bar graph for one factor model 2
  • corgraph: Correlogram
  • cor_ic: Plot Pearson correlation with interval of confidence
  • ibarplot.double: Invert letters for two factor chart
  • line_plot: Line chart
  • plot_cor: Plot correlation
  • plot_interaction: Interaction plot
  • plot_jitter: Column, box or segment chart with observations
  • plot_TH: Climate chart of temperature and humidity
  • plot_TH1: Climate chart of temperature and humidity (Model 2)
  • radargraph: Circular column chart
  • seg_graph: Segment graph for one factor
  • seg_graph2: Point graph for one factor model 2
  • sk_graph: Scott-Knott graphics
  • spider_graph: Spider graph for sensorial analysis
  • TBARPLOT.reverse: Reverse graph of DICT, DBCT and DQL output when geom="bar"
  • plot_tonetest: Graphic for t test to compare means with a reference value

Utils

  • sketch: Experimental sketch
  • aacp: Area under the curve
  • transf: Data transformation (Box-Cox, 1964)
  • summarise_anova: Summary of analysis of variance and test of means
  • summarise_dunnett: Dunnett's Test Summary
  • confinterval: Interval of confidence for groups

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Version

Install

install.packages('AgroR')

Monthly Downloads

1,406

Version

1.3.6

License

GPL (>= 2)

Maintainer

Gabriel Danilo Shimizu

Last Published

April 24th, 2024

Functions in AgroR (1.3.6)

PCA_function

Analysis: Principal components analysis
FAT3DIC.ad

Analysis: DIC experiments in triple factorial with aditional
FAT2DBC.ad

Analysis: DBC experiment in double factorial design with an additional treatment
FAT3DBC.ad

Analysis: DBC experiments in triple factorial with aditional
PSUBDBC

Analysis: DBC experiments in split-plot
FAT2DIC

Analysis: DIC experiments in double factorial
FAT3DBC

Analysis: DBC experiments in triple factorial
FAT2DIC.ad

Analysis: DIC experiment in double factorial design with an additional treatment
FAT3DIC

Analysis: DIC experiments in triple factorial
bar_dunnett

Graph: Barplot for Dunnett test
PSUBDIC

Analysis: DIC experiments in split-plot
PSUBSUBDBC

Analysis: DBC experiments in split-split-plot
bar_graph2

Graph: Bar graph for one factor model 2
aacp

Utils: Area under the curve
PSUBFAT2DBC

Analysis: Plot subdivided into randomized blocks with a subplot in a double factorial scheme
aristolochia

Dataset: Germination of seeds of Aristolochia sp. as a function of temperature.
bar_graph

Graph: Bar graph for one factor
cloro

Dataset: Sodium dichloroisocyanurate in soybean
barfacet

Graph: Bar graph for one factor with facets
barplot_positive

Graph: Positive barplot
bean

Dataset: Bean
cor_ic

Graph: Plot Pearson correlation with interval of confidence
dispvar

Descriptive: Boxplot with standardized data
STRIPLOT

Analysis: DBC experiments in strip-plot
conjfat2dbc

Analysis: Joint analysis of experiments in randomized block design in scheme factorial double
dic.ad

Analysis: Completely randomized design with an additional treatment for quantitative factor
confinterval

Utils: Interval of confidence for groups
bargraph_onefactor

Graph: Group DIC, DBC and DQL functions column charts
desd_fat2_quant_ad

Analysis: Regression analysis by orthogonal polynomials for double factorial scheme with additional control
bargraph_twofactor

Graph: Group FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC functions column charts
desc3fat

Descriptive: Descriptive analysis (Three factors)
covercrops

Dataset: Covercrops
corgraph

Graph: Correlogram
dbc.ad

Analysis: Randomized block design with an additional treatment for quantitative factor
grid.onefactor

utils: group graphs of the output of simple experiments in dic, dbc or dql
corn

Dataset: Corn
ibarplot.double

Graph: Invert letters for two factor chart
jointcluster

Analysis: Method to evaluate similarity of experiments based on QMres
emerg

Dataset: Emergence of passion fruit seeds over time .
mirtilo

Dataset: Cutting blueberry data
orchard

Dataset: Orchard
laranja

Dataset: Orange plants under different rootstocks
enxofre

Dataset: Sulfur data
plot_TH1

Graph: Climate chart of temperature and humidity (Model 2)
plot_TH

Graph: Climate chart of temperature and humidity
phao

Dataset: Osmocote in Phalaenopsis sp.
polynomial2

Analysis: Linear regression graph in double factorial
plot_tonetest

Graphics: Graphic for t test to compare means with a reference value
polynomial

Analysis: Linear regression graph
polynomial2_color

Analysis: Linear regression graph in double factorial with color graph
plot_cor

Graph: Plot correlation
passiflora

Dataset: Substrate data in the production of passion fruit seedlings
fat2_table

Utils: Summary of the analysis for factor arrangement with two qualitative factors.
pepper

Dataset: Pepper
pomegranate

Dataset: Pomegranate data
TBARPLOT.reverse

Graph: Reverse graph of DICT, DBCT and DQL output when geom="bar"
eucalyptus

Dataset: Eucaliptus grandis Barbin (2013)
quant.fat2.desd

Analysis: Polynomial splitting for double factorial in DIC and DBC
conjdbc

Analysis: Joint analysis of experiments in randomized block design
conjdic

Analysis: Joint analysis of experiments in completely randomized design
tonetest

Analysis: t test to compare means with a reference value
spider_graph

Graph: Spider graph for sensorial analysis
seg_graph2

Graph: Point graph for one factor model 2
weather

Dataset: Weather data
porco

Dataset: Pig development and production
soybean

Dataset: Soybean
seg_graph

Graph: Point graph for one factor
sk_graph

Graph: Scott-Knott graphics
radargraph

Graph: Circular column chart
transf

Utils: Data transformation (Box-Cox, 1964)
desc

Descriptive: Descriptive analysis
desc2fat

Descriptive: Descriptive analysis (Two factors)
sketch

Utils: Experimental sketch
sensorial

Dataset: Sensorial data
summarise_anova

Utils: Summary of Analysis of Variance and Test of Means
summarise_conj

Utils: Summary of Analysis of Variance and Test of Means for Joint analysis
simulate1

Dataset: Simulated data dict
dunn

Analysis: Post-hoc Dunn
tabledesc

Descriptive: Table descritive analysis
summarise_dunnett

Utils: Dunnett's Test Summary
test_two

Analysis: Test for two samples
tomate

Dataset: Tomato data
simulate3

Dataset: Simulated data dqlt
plot_jitter

Graph: Column, box or segment chart with observations
dunnett

Analysis: Dunnett test
line_plot

Graph: Line chart
plot_interaction

Graph: Interaction plot
logistic

Analysis: Logistic regression
simulate2

Dataset: Simulated data dbct
AgroR-package

AgroR: Experimental Statistics and Graphics for Agricultural Sciences
DIC.glm

Analysis: Completely randomized design by glm
DICT

Analysis: Completely randomized design evaluated over time
DBC

Analysis: Randomized block design
DBC.glm

Analysis: Randomized block design by glm
DQLT

Analysis: Latin square design evaluated over time
DQL

Analysis: Latin square design
DIC

Analysis: Completely randomized design
DBCT

Analysis: Randomized block design evaluated over time
FAT2DBC

Analysis: DBC experiments in double factorial