Type:	Package
Title:	Experimental Statistics and Graphics for Agricultural Sciences
Version:	1.3.7
Date:	2025-06-10
Maintainer:	Gabriel Danilo Shimizu <gabrield.shimizu@gmail.com>
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). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) <doi:10.4025/actasciagron.v47i1.73889>.
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	knitr, ggplot2, nortest, lme4, crayon, lmtest, emmeans, multcomp, ggrepel, MASS, cowplot, multcompView, RColorBrewer, drc, dunn.test, gtools, gridExtra
Suggests:	rmarkdown, roxygen2
Depends:	R (≥ 3.6.0)
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://agronomiar.github.io/AgroR_package/index.html, https://fisher.uel.br/AgroR_shiny, https://fisher.uel.br/AgroR_shiny.pt
NeedsCompilation:	no
Packaged:	2025-07-01 19:52:47 UTC; Administrador
Author:	Gabriel Danilo Shimizu [aut, cre], Rodrigo Yudi Palhaci Marubayashi [aut, ctb], Leandro Simoes Azeredo Goncalves [aut, ctb]
Repository:	CRAN
Date/Publication:	2025-07-02 08:50:05 UTC

AgroR: Experimental Statistics and Graphics for Agricultural Sciences

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). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) doi:10.4025/actasciagron.v47i1.73889.

Author(s)

Maintainer: Gabriel Danilo Shimizu gabrield.shimizu@gmail.com (ORCID)

Authors:

• Rodrigo Yudi Palhaci Marubayashi (ORCID) [contributor]

• Leandro Simoes Azeredo Goncalves (ORCID) [contributor]

See Also

Useful links:

• https://agronomiar.github.io/AgroR_package/index.html

• https://fisher.uel.br/AgroR_shiny

• https://fisher.uel.br/AgroR_shiny.pt

Analysis: Randomized block design

Description

This is a function of the AgroR package for statistical analysis of experiments conducted in a randomized block and balanced design with a factor considering the fixed model. The function presents the option to use non-parametric method or transform the dataset.

Usage

DBC(
  trat,
  block,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = TRUE,
  mcomp = "tukey",
  grau = 1,
  transf = 1,
  constant = 0,
  test = "parametric",
  geom = "bar",
  theme = theme_classic(),
  sup = NA,
  CV = TRUE,
  ylab = "response",
  xlab = "",
  textsize = 12,
  labelsize = 4,
  fill = "lightblue",
  angle = 0,
  family = "sans",
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey ("tukey"), LSD ("lsd"), Scott-Knott ("sk") or Duncan ("duncan")) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. Non-parametric analysis can be used by the Friedman test. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

Enable ggplot2 package to change theme argument.

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

CV and p-value of the graph indicate coefficient of variation and p-value of the F test of the analysis of variance.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

DIC, DQL

Examples

library(AgroR)

#=============================
# Example laranja
#=============================
data(laranja)
attach(laranja)
DBC(trat, bloco, resp, mcomp = "sk", angle=45, ylab = "Number of fruits/plants")

#=============================
# Friedman test
#=============================
DBC(trat, bloco, resp, test="noparametric", ylab = "Number of fruits/plants")

#=============================
# Example soybean
#=============================
data(soybean)
with(soybean, DBC(cult, bloc, prod,ylab="Grain yield (kg ha^-1)"))

Analysis: Randomized block design by glm

Description

Statistical analysis of experiments conducted in a randomized block design using a generalized linear model. It performs the deviance analysis and the effect is tested by a chi-square test. Multiple comparisons are adjusted by Tukey.

Usage

DBC.glm(
  trat,
  block,
  response,
  glm.family = "binomial",
  quali = TRUE,
  alpha.f = 0.05,
  alpha.t = 0.05,
  geom = "bar",
  theme = theme_classic(),
  sup = NA,
  ylab = "Response",
  xlab = "",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  textsize = 12,
  labelsize = 5,
  dec = 3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  angle.label = 0
)

Arguments

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("aristolochia")
attach(aristolochia)
# Assuming the same aristolochia data set, but considering randomized blocks
bloco=rep(paste("B",1:16),5)
resp=resp/2
DBC.glm(trat,bloco, cbind(resp,50-resp), glm.family="binomial")

Analysis: Randomized block design evaluated over time

Description

Function of the AgroR package for analysis of experiments conducted in a balanced qualitative, single-factorial randomized block design with multiple assessments over time, however without considering time as a factor.

Usage

DBCT(
  trat,
  block,
  time,
  response,
  alpha.f = 0.05,
  alpha.t = 0.05,
  mcomp = "tukey",
  geom = "bar",
  theme = theme_classic(),
  fill = "gray",
  ylab = "Response",
  xlab = "Independent",
  textsize = 12,
  labelsize = 5,
  pointsize = 4.5,
  error = TRUE,
  family = "sans",
  sup = 0,
  addmean = FALSE,
  posi = c(0.1, 0.8),
  legend = "Legend",
  ylim = NA,
  width.bar = 0.2,
  size.bar = 0.8,
  dec = 3,
  xnumeric = FALSE,
  all.letters = FALSE
)

Arguments

Details

The p-value of the analysis of variance, the normality test for Shapiro-Wilk errors, the Bartlett homogeneity test of variances, the independence of Durbin-Watson errors and the multiple comparison test (Tukey, Scott-Knott, LSD or Duncan).

Value

The function returns the p-value of Anova, the assumptions of normality of errors, homogeneity of variances and independence of errors, multiple comparison test, as well as a line graph

Note

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Gonçalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

See Also

DBC, DICT, DQLT

Examples

rm(list=ls())
data(simulate2)
attach(simulate2)

#===================================
# default
#===================================
DBCT(trat, bloco, tempo, resp)
DBCT(trat, bloco, tempo, resp,fill="rainbow")

#===================================
# segment chart
#===================================
DBCT(trat, bloco, tempo, resp, geom="point")

Analysis: Completely randomized design

Description

Statistical analysis of experiments conducted in a completely randomized and balanced design with a factor considering the fixed model. The function presents the option to use non-parametric method or transform the dataset.

Usage

DIC(
  trat,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = TRUE,
  mcomp = "tukey",
  grau = 1,
  transf = 1,
  constant = 0,
  test = "parametric",
  mcompNP = "LSD",
  p.adj = "holm",
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  sup = NA,
  CV = TRUE,
  xlab = "",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  textsize = 12,
  labelsize = 4,
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey ("tukey"), LSD ("lsd"), Scott-Knott ("sk") or Duncan ("duncan")) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. Non-parametric analysis can be used by the Kruskal-Wallis test. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

Enable ggplot2 package to change theme argument.

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

Post hoc test in nonparametric is using the criterium Fisher's least significant difference (p-adj="holm").

CV and p-value of the graph indicate coefficient of variation and p-value of the F test of the analysis of variance.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

W.J. Conover, Practical Nonparametrics Statistics. 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Hothorn, T. et al. Package ‘lmtest’. Testing linear regression models. https://cran. r-project. org/web/packages/lmtest/lmtest. pdf. Accessed, v. 6, 2015.

See Also

DBC DQL

Examples

library(AgroR)
data(pomegranate)

with(pomegranate, DIC(trat, WL, ylab = "Weight loss ('%')")) # tukey
with(pomegranate, DIC(trat, WL, mcomp = "sk", ylab = "Weight loss ('%')"))
with(pomegranate, DIC(trat, WL, mcomp = "duncan", ylab = "Weight loss ('%')"))

#=============================
# Kruskal-Wallis
#=============================
with(pomegranate, DIC(trat, WL, test = "noparametric", ylab = "Weight loss ('%')"))


#=============================
# chart type
#=============================
with(pomegranate, DIC(trat, WL, geom="point", ylab = "Weight loss ('%')"))
with(pomegranate, DIC(trat, WL, ylab = "Weight loss ('%')", xlab="Treatments"))

#=============================
# quantitative factor
#=============================
data("phao")
with(phao, DIC(dose,comp,quali=FALSE,grau=2,xlab = "Dose (g vase^-1)",ylab="Leaf length (cm)"))

#=============================
# data transformation
#=============================
data("pepper")
with(pepper, DIC(Acesso, VitC, transf = 0,ylab="Vitamin C"))

Analysis: Completely randomized design by glm

Description

Statistical analysis of experiments conducted in a completely randomized design using a generalized linear model. It performs the deviance analysis and the effect is tested by a chi-square test. Multiple comparisons are adjusted by Tukey.

Usage

DIC.glm(
  trat,
  response,
  glm.family = "binomial",
  quali = TRUE,
  alpha.f = 0.05,
  alpha.t = 0.05,
  geom = "bar",
  theme = theme_classic(),
  sup = NA,
  ylab = "Response",
  xlab = "",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  textsize = 12,
  labelsize = 5,
  dec = 3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  angle.label = 0
)

Arguments

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("aristolochia")
attach(aristolochia)
#=============================
# Use the DIC function
#=============================
DIC(trat, resp)

#=============================
# Use the DIC function noparametric
#=============================
DIC(trat, resp, test="noparametric")

#=============================
# Use the DIC.glm function
#=============================

resp=resp/4 # total germinated seeds

# the value 25 is the total of seeds in the repetition
DIC.glm(trat, cbind(resp,25-resp), glm.family="binomial")

Analysis: Completely randomized design evaluated over time

Description

Function of the AgroR package for the analysis of experiments conducted in a completely randomized, qualitative, uniform qualitative design with multiple assessments over time, however without considering time as a factor.

Usage

DICT(
  trat,
  time,
  response,
  alpha.f = 0.05,
  alpha.t = 0.05,
  mcomp = "tukey",
  theme = theme_classic(),
  geom = "bar",
  xlab = "Independent",
  ylab = "Response",
  p.adj = "holm",
  dec = 3,
  fill = "gray",
  error = TRUE,
  textsize = 12,
  labelsize = 5,
  pointsize = 4.5,
  family = "sans",
  sup = 0,
  addmean = FALSE,
  legend = "Legend",
  ylim = NA,
  width.bar = 0.2,
  size.bar = 0.8,
  posi = c(0.1, 0.8),
  xnumeric = FALSE,
  all.letters = FALSE
)

Arguments

Value

The function returns the p-value of Anova, the assumptions of normality of errors, homogeneity of variances and independence of errors, multiple comparison test, as well as a line graph

Note

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

See Also

DIC, DBCT, DQLT

Examples

rm(list=ls())
data(simulate1)
attach(simulate1)
with(simulate1, DICT(trat, tempo, resp))
with(simulate1, DICT(trat, tempo, resp, fill="rainbow",family="serif"))
with(simulate1, DICT(trat, tempo, resp,geom="bar",sup=40))
with(simulate1, DICT(trat, tempo, resp,geom="point",sup=40))

Analysis: Latin square design

Description

This is a function of the AgroR package for statistical analysis of experiments conducted in Latin Square and balanced design with a factor considering the fixed model.

Usage

DQL(
  trat,
  line,
  column,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = TRUE,
  mcomp = "tukey",
  grau = 1,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  sup = NA,
  CV = TRUE,
  ylab = "Response",
  xlab = "",
  textsize = 12,
  labelsize = 4,
  fill = "lightblue",
  angle = 0,
  family = "sans",
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey ("tukey"), LSD ("lsd"), Scott-Knott ("sk") or Duncan ("duncan")) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

CV and p-value of the graph indicate coefficient of variation and p-value of the F test of the analysis of variance.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

DIC, DBC

Examples

library(AgroR)
data(porco)
with(porco, DQL(trat, linhas, colunas, resp, ylab="Weigth (kg)"))

Analysis: Latin square design evaluated over time

Description

Function of the AgroR package for the analysis of experiments conducted in a balanced qualitative single-square Latin design with multiple assessments over time, however without considering time as a factor.

Usage

DQLT(
  trat,
  line,
  column,
  time,
  response,
  alpha.f = 0.05,
  alpha.t = 0.05,
  mcomp = "tukey",
  error = TRUE,
  xlab = "Independent",
  ylab = "Response",
  textsize = 12,
  labelsize = 5,
  pointsize = 4.5,
  family = "sans",
  sup = 0,
  addmean = FALSE,
  posi = c(0.1, 0.8),
  geom = "bar",
  fill = "gray",
  legend = "Legend",
  ylim = NA,
  width.bar = 0.2,
  size.bar = 0.8,
  dec = 3,
  theme = theme_classic(),
  xnumeric = FALSE,
  all.letters = FALSE
)

Arguments

Details

The p-value of the analysis of variance, the normality test for Shapiro-Wilk errors, the Bartlett homogeneity test of variances, the independence of Durbin-Watson errors and the multiple comparison test ( Tukey, Scott-Knott, LSD or Duncan).

Value

The function returns the p-value of Anova, the assumptions of normality of errors, homogeneity of variances and independence of errors, multiple comparison test, as well as a line graph

Note

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

See Also

DQL, DICT, DBCT

Examples

rm(list=ls())
data(simulate3)
attach(simulate3)
DQLT(trat, linhas, colunas, tempo, resp)

Analysis: DBC experiments in double factorial

Description

Analysis of an experiment conducted in a randomized block design in a double factorial scheme using analysis of variance of fixed effects.

Usage

FAT2DBC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  legend = "Legend",
  fill = "lightblue",
  angle = 0,
  textsize = 12,
  labelsize = 4,
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  family = "sans",
  point = "mean_sd",
  addmean = TRUE,
  errorbar = TRUE,
  CV = TRUE,
  sup = NA,
  color = "rainbow",
  posi = "right",
  ylim = NA,
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett or Levene), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two quantitative factors.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

FAT2DBC.ad

Examples

#================================================
# Example cloro
#================================================
library(AgroR)
data(cloro)
with(cloro,FAT2DBC(f1, f2, bloco, resp, ylab="Number of nodules"))
with(cloro,FAT2DBC(f1, f2, bloco, resp, mcomp="sk", ylab="Number of nodules"))
#================================================
# Example covercrops
#================================================
library(AgroR)
data(covercrops)
with(covercrops,FAT2DBC(A, B, Bloco, Resp, ylab="Yield (Kg 100 m^2)"))
with(covercrops,FAT2DBC(A, B, Bloco, Resp, mcomp="sk", ylab="Yield (Kg 100 m^2)"))

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

Description

Analysis of an experiment conducted in a randomized block design in a double factorial scheme using analysis of variance of fixed effects.

Usage

FAT2DBC.ad(
  f1,
  f2,
  block,
  response,
  responseAd,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  ad.label = "Additional",
  color = "rainbow",
  fill = "lightblue",
  textsize = 12,
  labelsize = 4,
  addmean = TRUE,
  errorbar = TRUE,
  CV = TRUE,
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  angle = 0,
  posi = "right",
  family = "sans",
  point = "mean_sd",
  sup = NA,
  ylim = NA,
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett or Levene), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two quantitative factors.

The assumptions of variance analysis disregard additional treatment

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

FAT2DBC

dunnett

Examples

library(AgroR)
data(cloro)
respAd=c(268, 322, 275, 350, 320)
with(cloro, FAT2DBC.ad(f1, f2, bloco, resp, respAd, ylab="Number of nodules"))

Analysis: DIC experiments in double factorial

Description

Analysis of an experiment conducted in a completely randomized design in a double factorial scheme using analysis of variance of fixed effects.

Usage

FAT2DIC(
  f1,
  f2,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  lab.factor = c("F1", "F2"),
  color = "rainbow",
  fill = "lightblue",
  textsize = 12,
  labelsize = 4,
  addmean = TRUE,
  errorbar = TRUE,
  CV = TRUE,
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  angle = 0,
  posi = "right",
  family = "sans",
  point = "mean_sd",
  sup = NA,
  ylim = NA,
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett or Levene), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two quantitative factors.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., & de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

FAT2DIC.ad

Examples

#====================================
# Example cloro
#====================================
library(AgroR)
data(cloro)
with(cloro, FAT2DIC(f1, f2, resp, ylab="Number of nodules", names.fat = c("Inoculation","Stages")))

#====================================
# Example corn
#====================================
library(AgroR)
data(corn)
with(corn, FAT2DIC(A, B, Resp, quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
with(corn, FAT2DIC(A, B, Resp, mcomp="sk", quali=c(TRUE, TRUE),ylab="Heigth (cm)"))

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

Description

Analysis of an experiment conducted in a completely randomized design in a double factorial scheme using analysis of variance of fixed effects.

Usage

FAT2DIC.ad(
  f1,
  f2,
  repe,
  response,
  responseAd,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  ad.label = "Additional",
  color = "rainbow",
  fill = "lightblue",
  textsize = 12,
  labelsize = 4,
  addmean = TRUE,
  errorbar = TRUE,
  CV = TRUE,
  dec = 3,
  width.column = 0.9,
  width.bar = 0.3,
  angle = 0,
  posi = "right",
  family = "sans",
  point = "mean_sd",
  sup = NA,
  ylim = NA,
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett or Levene), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two quantitative factors.

The assumptions of variance analysis disregard additional treatment

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., & de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

FAT2DIC

dunnett

Examples

library(AgroR)
data(cloro)
respAd=c(268, 322, 275, 350, 320)
with(cloro, FAT2DIC.ad(f1, f2, bloco, resp, respAd, ylab="Number of nodules"))

Analysis: DBC experiments in triple factorial

Description

Analysis of an experiment conducted in a randomized block design in a triple factorial scheme using analysis of variance of fixed effects.

Usage

FAT3DBC(
  f1,
  f2,
  f3,
  block,
  response,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  transf = 1,
  constant = 0,
  names.fat = c("F1", "F2", "F3"),
  ylab = "Response",
  sup = NA,
  grau = c(NA, NA, NA),
  grau12 = NA,
  grau13 = NA,
  grau23 = NA,
  grau21 = NA,
  grau31 = NA,
  grau32 = NA,
  grau123 = NA,
  grau213 = NA,
  grau312 = NA,
  fill = "lightblue",
  theme = theme_classic(),
  angulo = 0,
  errorbar = TRUE,
  addmean = TRUE,
  family = "sans",
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  point = "mean_sd",
  angle.label = 0
)

Arguments

Value

The analysis of variance table, the Shapiro-Wilk error normality test, the Bartlett homogeneity test of variances, the Durbin-Watson error independence test, multiple comparison test (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.For significant triple interaction only, no graph is returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two or more quantitative factors. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Ferreira, E. B., Cavalcanti, P. P., and Nogueira, D. A. (2014). ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5(19), 2952.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Examples

library(AgroR)
data(enxofre)
with(enxofre, FAT3DBC(f1, f2, f3, bloco, resp))

Analysis: DBC experiments in triple factorial with aditional

Description

Analysis of an experiment conducted in a randomized block design in a triple factorial scheme with one aditional control using analysis of variance of fixed effects.

Usage

FAT3DBC.ad(
  f1,
  f2,
  f3,
  block,
  response,
  responseAd,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  transf = 1,
  constant = 0,
  names.fat = c("F1", "F2", "F3"),
  ylab = "Response",
  sup = NA,
  grau = c(NA, NA, NA),
  grau12 = NA,
  grau13 = NA,
  grau23 = NA,
  grau21 = NA,
  grau31 = NA,
  grau32 = NA,
  grau123 = NA,
  grau213 = NA,
  grau312 = NA,
  fill = "lightblue",
  theme = theme_classic(),
  ad.label = "Additional",
  angulo = 0,
  errorbar = TRUE,
  addmean = TRUE,
  family = "sans",
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  point = "mean_sd",
  angle.label = 0
)

Arguments

Value

The analysis of variance table, the Shapiro-Wilk error normality test, the Bartlett homogeneity test of variances, the Durbin-Watson error independence test, multiple comparison test (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.For significant triple interaction only, no graph is returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two or more quantitative factors. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Ferreira, E. B., Cavalcanti, P. P., and Nogueira, D. A. (2014). ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5(19), 2952.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Examples

library(AgroR)
data(enxofre)
respAd=c(2000,2400,2530,2100)
attach(enxofre)
with(enxofre, FAT3DBC.ad(f1, f2, f3, bloco, resp, respAd))

Analysis: DIC experiments in triple factorial

Description

Analysis of an experiment conducted in a completely randomized design in a triple factorial scheme using analysis of variance of fixed effects.

Usage

FAT3DIC(
  f1,
  f2,
  f3,
  response,
  norm = "sw",
  alpha.t = 0.05,
  alpha.f = 0.05,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  grau = c(NA, NA, NA),
  grau12 = NA,
  grau13 = NA,
  grau23 = NA,
  grau21 = NA,
  grau31 = NA,
  grau32 = NA,
  grau123 = NA,
  grau213 = NA,
  grau312 = NA,
  transf = 1,
  constant = 0,
  names.fat = c("F1", "F2", "F3"),
  ylab = "Response",
  sup = NA,
  fill = "lightblue",
  theme = theme_classic(),
  angulo = 0,
  family = "sans",
  addmean = TRUE,
  errorbar = TRUE,
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  point = "mean_sd",
  angle.label = 0
)

Arguments

Value

The analysis of variance table, the Shapiro-Wilk error normality test, the Bartlett homogeneity test of variances, the Durbin-Watson error independence test, multiple comparison test (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.For significant triple interaction only, no graph is returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two or more quantitative factors. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Ferreira, E. B., Cavalcanti, P. P., and Nogueira, D. A. (2014). ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5(19), 2952.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Examples

library(AgroR)
data(enxofre)
with(enxofre, FAT3DIC(f1, f2, f3, resp))

Analysis: DIC experiments in triple factorial with aditional

Description

Analysis of an experiment conducted in a completely randomized design in a triple factorial scheme with one aditional control using analysis of variance of fixed effects.

Usage

FAT3DIC.ad(
  f1,
  f2,
  f3,
  repe,
  response,
  responseAd,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  transf = 1,
  constant = 0,
  names.fat = c("F1", "F2", "F3"),
  ylab = "Response",
  sup = NA,
  grau = c(NA, NA, NA),
  grau12 = NA,
  grau13 = NA,
  grau23 = NA,
  grau21 = NA,
  grau31 = NA,
  grau32 = NA,
  grau123 = NA,
  grau213 = NA,
  grau312 = NA,
  fill = "lightblue",
  theme = theme_classic(),
  ad.label = "Additional",
  angulo = 0,
  errorbar = TRUE,
  addmean = TRUE,
  family = "sans",
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  point = "mean_sd",
  angle.label = 0
)

Arguments

Value

The analysis of variance table, the Shapiro-Wilk error normality test, the Bartlett homogeneity test of variances, the Durbin-Watson error independence test, multiple comparison test (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.For significant triple interaction only, no graph is returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two or more quantitative factors. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Ferreira, E. B., Cavalcanti, P. P., and Nogueira, D. A. (2014). ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5(19), 2952.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Examples

library(AgroR)
data(enxofre)
respAd=c(2000,2400,2530,2100)
with(enxofre, FAT3DIC.ad(f1, f2, f3, bloco, resp, respAd))

Analysis: Principal components analysis

Description

This function performs principal component analysis.

Usage

PCA_function(
  data,
  scale = TRUE,
  text = TRUE,
  pointsize = 5,
  textsize = 12,
  labelsize = 4,
  linesize = 0.6,
  repel = TRUE,
  ylab = NA,
  xlab = NA,
  groups = NA,
  sc = 1,
  font.family = "sans",
  theme = theme_bw(),
  label.legend = "Cluster",
  type.graph = "biplot"
)

Arguments

Details

The type.graph argument defines the graph that will be returned, in the case of "biplot" the biplot graph is returned with the first two main components and with eigenvalues and eigenvectors. In the case of "scores" only the treatment scores are returned, while for "cor" the correlations are returned. For "corPCA" a correlation between the vectors with the components is returned.

Value

The eigenvalues and eigenvectors, the explanation percentages of each principal component, the correlations between the vectors with the principal components, as well as graphs are returned.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Examples

data(pomegranate)
medias=tabledesc(pomegranate)
PCA_function(medias)

Analysis: DBC experiments in split-plot

Description

Analysis of an experiment conducted in a randomized block design in a split-plot scheme using fixed effects analysis of variance.

Usage

PSUBDBC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  color = "rainbow",
  textsize = 12,
  labelsize = 4,
  dec = 3,
  errorbar = TRUE,
  addmean = TRUE,
  ylim = NA,
  point = "mean_se",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  posi = "right",
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett), the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

#==============================
# Example tomate
#==============================
library(AgroR)
data(tomate)
with(tomate, PSUBDBC(parc, subp, bloco, resp, ylab="Dry mass (g)"))

#==============================
# Example orchard
#==============================
library(AgroR)
data(orchard)
with(orchard, PSUBDBC(A, B, Bloco, Resp, ylab="CBM"))

Analysis: DIC experiments in split-plot

Description

Analysis of an experiment conducted in a completely randomized design in a split-plot scheme using fixed effects analysis of variance.

Usage

PSUBDIC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  lab.factor = c("F1", "F2"),
  fill = "lightblue",
  angle = 0,
  family = "sans",
  color = "rainbow",
  errorbar = TRUE,
  addmean = TRUE,
  textsize = 12,
  labelsize = 4,
  dec = 3,
  ylim = NA,
  posi = "right",
  point = "mean_se",
  angle.label = 0,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett), the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

#===================================
# Example tomate
#===================================
# Obs. Consider that the "tomato" experiment is a completely randomized design.
library(AgroR)
data(tomate)
with(tomate, PSUBDIC(parc, subp, bloco, resp, ylab="Dry mass (g)"))

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

Description

This function performs the analysis of a randomized block design in a split-plot with a subplot in a double factorial scheme.

Usage

PSUBFAT2DBC(
  f1,
  f2,
  f3,
  block,
  resp,
  alpha.f = 0.05,
  alpha.t = 0.05,
  norm = "sw",
  homog = "bt",
  mcomp = "tukey"
)

Arguments

Value

Analysis of variance of fixed effects and multiple comparison test of Tukey, Scott-Knott, LSD or Duncan.

Examples

f1=rep(c("PD","PDE","C"), e = 40);f1=factor(f1,unique(f1))
f2=rep(c(300,400), e = 20,3);f2=factor(f2,unique(f2))
f3=rep(c("c1", "c2", "c3", "c4"), e = 5,6);f3=factor(f3,unique(f3))
bloco=rep(paste("B",1:5),24); bloco=factor(bloco,unique(bloco))
set.seed(10)
resp=rnorm(120,50,5)
PSUBFAT2DBC(f1,f2,f3,bloco,resp,alpha.f = 0.5) # force triple interaction
PSUBFAT2DBC(f1,f2,f3,bloco,resp,alpha.f = 0.4) # force double interaction

Analysis: DBC experiments in split-split-plot

Description

Analysis of an experiment conducted in a randomized block design in a split-split-plot scheme using analysis of variance of fixed effects.

Usage

PSUBSUBDBC(
  f1,
  f2,
  f3,
  block,
  response,
  alpha.f = 0.05,
  alpha.t = 0.05,
  dec = 3,
  mcomp = "tukey"
)

Arguments

Value

Analysis of variance of fixed effects and multiple comparison test of Tukey, LSD or Duncan.

Note

The PSUBSUBDBC function does not present residual analysis, interaction breakdown, graphs and implementations of various multiple comparison or regression tests. The function only returns the analysis of variance and multiple comparison test of Tukey, LSD or Duncan.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data(enxofre)
with(enxofre, PSUBSUBDBC(f1, f2, f3, bloco, resp))

Analysis: DBC experiments in strip-plot

Description

Analysis of an experiment conducted in a block randomized design in a strit-plot scheme using fixed effects analysis of variance.

Usage

STRIPLOT(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  alpha.f = 0.05,
  transf = 1,
  textsize = 12,
  labelsize = 4,
  constant = 0
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett). The function also returns a standardized residual plot.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

#===================================
# Example tomate
#===================================
# Obs. Consider that the "tomato" experiment is a block randomized design in strip-plot.
library(AgroR)
data(tomate)
with(tomate, STRIPLOT(parc, subp, bloco, resp))

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

Description

The function performs the construction of a reverse graph on the output of DICT, DBCT and DQL when geom="bar".

Usage

TBARPLOT.reverse(plot.t)

Arguments

Value

Returns a reverse graph of the output of DICT, DBCT or DQLT when geom="bar".

Note

All layout and subtitles are imported from DICT, DBCT and DQLT functions

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

DICT, DBCT, DQLT

Examples

data(simulate1)
a=with(simulate1, DICT(trat, tempo, resp,geom="bar",sup=40))
TBARPLOT.reverse(a)

Utils: Area under the curve

Description

Performs the calculation of the area under the progress curve. Initially created for the plant disease area, whose name is "area under the disease progress curve", it can be adapted to various areas of agrarian science.

Usage

aacp(data)

Arguments

Value

Returns a vector with the area values under the curve

Note

Just enter the data. Exclude treatment columns. See example.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

References

Campbell, C. L., and Madden, L. V. (1990). Introduction to plant disease epidemiology. John Wiley and Sons.

See Also

transf, sketch

Examples

#=======================================
# Using the simulate1 dataset
#=======================================
data("simulate1")

# Converting to readable format for function
dados=cbind(simulate1[simulate1$tempo==1,3],
            simulate1[simulate1$tempo==2,3],
            simulate1[simulate1$tempo==3,3],
            simulate1[simulate1$tempo==4,3],
            simulate1[simulate1$tempo==5,3],
            simulate1[simulate1$tempo==6,3])
colnames(dados)=c(1,2,3,4,5,6)
dados

# Creating the treatment vector
resp=aacp(dados)
trat=simulate1$trat[simulate1$tempo==1]

# Analyzing by DIC function
DIC(trat,resp)

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

Description

The data come from an experiment conducted at the Seed Analysis Laboratory of the Agricultural Sciences Center of the State University of Londrina, in which five temperatures (15, 20, 25, 30 and 35C) were evaluated in the germination of Aristolochia elegans. The experiment was conducted in a completely randomized design with four replications of 25 seeds each.

Usage

data("aristolochia")

Format

data.frame containing data set

trat

numeric vector with factor 1

resp

Numeric vector with response

See Also

cloro, laranja, enxofre, laranja, mirtilo, passiflora, phao, porco, pomegranate, simulate1, simulate2, simulate3, tomate, weather

Examples

data(aristolochia)

Graph: Barplot for Dunnett test

Description

The function performs the construction of a column chart of Dunnett's test.

Usage

bar_dunnett(
  output.dunnett,
  ylab = "Response",
  xlab = "",
  fill = c("white", "#F8766D", "#00BFC4"),
  sup = NA,
  add.mean = TRUE,
  round = 2
)

Arguments

Value

Returns a column chart of Dunnett's test. The colors indicate difference from the control.

Examples

#====================================================
# randomized block design in factorial double
#====================================================
library(AgroR)
data(cloro)
attach(cloro)
respAd=c(268, 322, 275, 350, 320)
a=FAT2DBC.ad(f1, f2, bloco, resp, respAd,
             ylab="Number of nodules",mcomp="sk")
data=rbind(data.frame(trat=paste(f1,f2,sep = ""),bloco=bloco,resp=resp),
           data.frame(trat=c("Test","Test","Test","Test","Test"),
                      bloco=unique(bloco),resp=respAd))
a= with(data,dunnett(trat = trat,
                  resp = resp,
                  control = "Test",
                  block=bloco,model = "DBC"))
 bar_dunnett(a)

Graph: Bar graph for one factor

Description

This is a function of the bar graph for one factor

Usage

bar_graph(model, fill = "lightblue", horiz = TRUE, axis.0 = FALSE)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

barplot_positive, plot_TH, plot_TH1, corgraph, spider_graph, line_plot, plot_cor, plot_interaction, plot_jitter, seg_graph, TBARPLOT.reverse

Examples

data("laranja")
a=with(laranja, DBC(trat, bloco, resp,
     mcomp = "sk",angle=45,
     ylab = "Number of fruits/plants"))
bar_graph(a,horiz = FALSE)

Graph: Bar graph for one factor model 2

Description

This is a function of the bar graph for one factor

Usage

bar_graph2(
  model,
  point.color = "black",
  point.size = 2,
  point.shape = 16,
  text.color = "black",
  label.color = "black",
  bar.color = "black",
  title.size = 14,
  y.text = 0,
  add.info = NA,
  y.info = 0,
  width.bar = 0,
  color.info = "black",
  fill = "lightblue"
)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

barplot_positive, plot_TH, plot_TH1, corgraph, spider_graph, line_plot, plot_cor, plot_interaction, plot_jitter, seg_graph, TBARPLOT.reverse

Examples

data("laranja")
a=with(laranja, DBC(trat, bloco, resp,
     mcomp = "sk",angle=45,sup = 10,
     family = "serif",
     ylab = "Number of fruits/plants"))
bar_graph2(a)
bar_graph2(a,fill="darkblue",point.color="orange",text.color='white')

Graph: Bar graph for one factor with facets

Description

This is a function of the bar graph for one factor with facets

Usage

barfacet(
  model,
  facet = NULL,
  theme = theme_bw(),
  horiz = FALSE,
  geom = "bar",
  fill = "lightblue",
  pointsize = 4.5,
  facet.background = "gray80"
)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data("laranja")
a=with(laranja, DBC(trat, bloco, resp,
     mcomp = "sk",angle=45,sup = 10,family = "serif",
     ylab = "Number of fruits/plants"))
barfacet(a,c("S1","S1","S1","S1","S1",
             "S2","S2","S3","S3"))

Graph: Group DIC, DBC and DQL functions column charts

Description

Groups two or more column charts exported from DIC, DBC or DQL function

Usage

bargraph_onefactor(
  analysis,
  labels = NULL,
  ocult.facet = FALSE,
  ocult.box = FALSE,
  facet.size = 14,
  ylab = NULL,
  width.bar = 0.3,
  sup = NULL
)

Arguments

Value

Returns a column chart grouped by facets

Examples

library(AgroR)
data("laranja")
a=with(laranja, DBC(trat, bloco, resp, ylab = "Number of fruits/plants"))
b=with(laranja, DBC(trat, bloco, resp,  ylab = "Number of fruits/plants"))
c=with(laranja, DBC(trat, bloco, resp, ylab = "Number of fruits/plants"))
bargraph_onefactor(analysis = list(a,b,c), labels = c("One","Two","Three"),ocult.box = TRUE)

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

Description

Groups two or more column charts exported from FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC function

Usage

bargraph_twofactor(
  analysis,
  labels = NULL,
  ocult.facet = FALSE,
  ocult.box = FALSE,
  facet.size = 14,
  ylab = NULL,
  width.bar = 0.3,
  sup = NULL
)

Arguments

Value

Returns a column chart grouped by facets

Examples

library(AgroR)
data(corn)
a=with(corn, FAT2DIC(A, B, Resp, quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
b=with(corn, FAT2DIC(A, B, Resp, mcomp="sk", quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
bargraph_twofactor(analysis = list(a,b), labels = c("One","Two"),ocult.box = TRUE)

Graph: Positive barplot

Description

Column chart with two variables that assume a positive response and represented by opposite sides, such as dry mass of the area and dry mass of the root

Usage

barplot_positive(
  a,
  b,
  ylab = "Response",
  var_name = c("Var1", "Var2"),
  legend.title = "Variable",
  fill_color = c("darkgreen", "brown"),
  width.col = 0.9,
  width.bar = 0.2
)

Arguments

Value

The function returns a column chart with two positive sides

Note

When there is only an effect of the isolated factor in the case of factorial or subdivided plots, it is possible to use the barplot_positive function.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

See Also

sk_graph, plot_TH, corgraph, spider_graph, line_plot

Examples

data("passiflora")
attach(passiflora)
a=with(passiflora, DBC(trat, bloco, MSPA))
b=with(passiflora, DBC(trat, bloco, MSR))
barplot_positive(a, b, var_name = c("DMAP","DRM"), ylab = "Dry root (g)")

a=with(passiflora, DIC(trat, MSPA,test = "noparametric"))
b=with(passiflora, DIC(trat, MSR))
barplot_positive(a, b, var_name = c("DMAP","DRM"), ylab = "Dry root (g)")

Dataset: Bean

Description

An experiment to evaluate the effect of different strains of Azospirillum on common bean cultivar IPR Sabia was carried out in a greenhouse. A completely randomized design with five strains was used. of Azospirillum (treatments) and five repetitions. The response variable analyzed was grain production per plant (g plant-1).

Usage

data("bean")

Format

data.frame containing data set

trat

numeric vector with treatment

prod

Numeric vector with grain production per plant

See Also

aristolochia, cloro, laranja, enxofre, laranja, mirtilo, passiflora, phao, porco, pomegranate, simulate1, simulate2, simulate3, tomate, weather

Examples

data(bean)

Dataset: Sodium dichloroisocyanurate in soybean

Description

An experiment was conducted in a greenhouse in pots at the State University of Londrina. The work has the objective of evaluating the application of sodium dichloroisocyanurate (DUP) in soybean in 4 periods of application in soybean inoculated or not with Rhizobium and its influence on the number of nodules. The experiment was conducted in a completely randomized design with five replications.

Usage

data(cloro)

Format

data.frame containing data set

f1

Categorical vector with factor 1

f2

Categorical vector with factor 2

bloco

Categorical vector with block

resp

Numeric vector with number nodules

References

Rony Kauling Tonelli. Efeito do uso de dicloroisocianurato de sodio sobre a nodulacao em raizes de soja. 2016. Trabalho de Conclusao de Curso. (Graduacao em Agronomia) - Universidade Estadual de Londrina.

See Also

enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(cloro)

Utils: Interval of confidence for groups

Description

Calculates confidence interval for groups

Usage

confinterval(resp, group, alpha = 0.95, type = "upper")

Arguments

Value

returns a numeric vector with confidence interval grouped by treatment.

Examples

#===================================
# One factor
#===================================

dados=rnorm(100,10,1)
trat=rep(paste("T",1:10),10)
confinterval(dados,trat)

#===================================
# Two factor
#===================================
f1=rep(c("A","B"),e=50)
f2=rep(paste("T",1:5),e=10,2)
confinterval(dados,list(f1,f2))

Analysis: Joint analysis of experiments in randomized block design

Description

Function of the AgroR package for joint analysis of experiments conducted in a randomized qualitative or quantitative single-block design with balanced data.

Usage

conjdbc(
  trat,
  block,
  local,
  response,
  transf = 1,
  constant = 0,
  norm = "sw",
  homog = "bt",
  homog.value = 7,
  theme = theme_classic(),
  mcomp = "tukey",
  quali = TRUE,
  alpha.f = 0.05,
  alpha.t = 0.05,
  grau = NA,
  ylab = "response",
  title = "",
  xlab = "",
  fill = "lightblue",
  angulo = 0,
  textsize = 12,
  dec = 3,
  family = "sans",
  errorbar = TRUE
)

Arguments

Value

Returns the assumptions of the analysis of variance, the assumption of the joint analysis by means of a QMres ratio matrix, the analysis of variance, the multiple comparison test or regression.

Note

In this function there are three possible outcomes. When the ratio between the experiments is greater than 7, the separate analyzes are returned, without however using the square of the joint residue. When the ratio is less than 7, but with significant interaction, the effects are tested using the square of the joint residual. When there is no significant interaction and the ratio is less than 7, the joint analysis between the experiments is returned.

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Ferreira, P. V. Estatistica experimental aplicada a agronomia. Edufal, 2018.

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

library(AgroR)
data(mirtilo)

#===================================
# No significant interaction
#===================================
with(mirtilo, conjdbc(trat, bloco, exp, resp))

#===================================
# Significant interaction
#===================================
data(eucalyptus)
with(eucalyptus, conjdbc(trati, bloc, exp, resp))

Analysis: Joint analysis of experiments in completely randomized design

Description

Function of the AgroR package for joint analysis of experiments conducted in a completely randomized design with a qualitative or quantitative factor with balanced data.

Usage

conjdic(
  trat,
  repet,
  local,
  response,
  transf = 1,
  constant = 0,
  norm = "sw",
  homog = "bt",
  mcomp = "tukey",
  homog.value = 7,
  quali = TRUE,
  alpha.f = 0.05,
  alpha.t = 0.05,
  grau = NA,
  theme = theme_classic(),
  ylab = "response",
  title = "",
  xlab = "",
  color = "rainbow",
  fill = "lightblue",
  angulo = 0,
  textsize = 12,
  dec = 3,
  family = "sans",
  errorbar = TRUE
)

Arguments

Value

Returns the assumptions of the analysis of variance, the assumption of the joint analysis by means of a QMres ratio matrix, the analysis of variance, the multiple comparison test or regression.

Note

In this function there are three possible outcomes. When the ratio between the experiments is greater than 7, the separate analyzes are returned, without however using the square of the joint residue. When the ratio is less than 7, but with significant interaction, the effects are tested using the square of the joint residual. When there is no significant interaction and the ratio is less than 7, the joint analysis between the experiments is returned.

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Ferreira, P. V. Estatistica experimental aplicada a agronomia. Edufal, 2018.

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

library(AgroR)
data(mirtilo)
with(mirtilo, conjdic(trat, bloco, exp, resp))

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

Description

Function of the AgroR package for joint analysis of experiments conducted in a randomized factorial double in block design with balanced data. The function generates the joint analysis through two models. Model 1: F-test of the effects of Factor 1, Factor 2 and F1 x F2 interaction are used in reference to the mean square of the interaction with the year. Model 2: F-test of the Factor 1, Factor 2 and F1 x F2 interaction effects are used in reference to the mean square of the residual.

Usage

conjfat2dbc(
  f1,
  f2,
  block,
  experiment,
  response,
  transf = 1,
  constant = 0,
  model = 1,
  norm = "sw",
  homog = "bt",
  homog.value = 7,
  alpha.f = 0.05,
  alpha.t = 0.05
)

Arguments

Value

Returns the assumptions of the analysis of variance, the assumption of the joint analysis by means of a QMres ratio matrix and analysis of variance

Note

The function is still limited to analysis of variance and assumptions only.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

Ferreira, P. V. Estatistica experimental aplicada a agronomia. Edufal, 2018.

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Examples

library(AgroR)
ano=factor(rep(c(2018,2019,2020),e=48))
f1=rep(rep(c("A","B","C"),e=16),3)
f2=rep(rep(rep(c("a1","a2","a3","a4"),e=4),3),3)
resp=rnorm(48*3,10,1)
bloco=rep(c("b1","b2","b3","b4"),36)
dados=data.frame(ano,f1,f2,resp,bloco)
with(dados,conjfat2dbc(f1,f2,bloco,ano,resp, model=1))

Graph: Plot Pearson correlation with interval of confidence

Description

Plot Pearson correlation with interval of confidence

Usage

cor_ic(
  data,
  background = TRUE,
  axis.size = 12,
  ylab = "",
  xlab = "Correlation (r)",
  theme = theme_classic()
)

Arguments

Value

The function returns a new graphical approach to correlation.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("pomegranate")
cor_ic(pomegranate[,-1])

Graph: Correlogram

Description

Correlation analysis function (Pearson or Spearman)

Usage

corgraph(
  data,
  axissize = 12,
  legendsize = 12,
  legendposition = c(0.9, 0.2),
  legendtitle = "Correlation",
  method = "pearson",
  pallete = "RdBu",
  color.marginal = "gray50",
  size.tile.lty = 1,
  size.label.cor = 1,
  fill.label.cor = "lightyellow",
  font.family = "sans"
)

Arguments

Value

The function returns a correlation matrix

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("pomegranate")
corgraph(pomegranate[,-1])

Dataset: Corn

Description

A 3 x 2 factorial experiment was carried out to compare three new corn hybrids considering the change in sowing density, being 55 thousand or 65 thousand seeds per hectare. For this case, the researcher is not interested in estimating values for other densities, but only in verifying if one density differs from the other. The experiment was carried out according to a completely randomized design with 4 repetitions of each treatment.

Usage

data(corn)

Format

data.frame containing data set

A

Categorical vector with hybrids

B

Categorical vector with density

resp

Numeric vector with response

See Also

enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(corn)

Dataset: Covercrops

Description

Consider a 3 ×3 factorial experiment in randomized blocks, with 4 replications, on the influence of three new soybean cultivars (A1, A2 and A3) and the use of three types of green manure (B1, B2 and B3) on yield in 100 m2 plots.

Usage

data(covercrops)

Format

data.frame containing data set

A

Categorical vector with cultivars

B

Categorical vector with green manure

Bloco

Categorical vector with block

Resp

Numeric vector with yield

See Also

enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(covercrops)

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

Description

Statistical analysis of experiments conducted in a randomized block design with an additional treatment and balanced design with a factor considering the fixed model.

Usage

dbc.ad(
  trat,
  block,
  response,
  responsead,
  grau = 1,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  theme = theme_classic(),
  ylab = "response",
  xlab = "independent",
  family = "sans",
  posi = "top",
  pointsize = 4.5,
  linesize = 0.8,
  width.bar = NA,
  point = "mean_sd"
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, adjustment of regression models up to grade 3 polynomial. The function also returns a standardized residual plot.

Note

In some experiments, the researcher may study a quantitative factor, such as fertilizer doses, and present a control, such as a reference fertilizer, treated as a qualitative control. In these cases, there is a difference between considering only the residue in the unfolding of the polynomial, removing or not the qualitative treatment, or since a treatment is excluded from the analysis. In this approach, the residue used is also considering the qualitative treatment, a method similar to the factorial scheme with additional control.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

doses = c(rep(c(1:5),e=3))
resp = c(3, 4, 3, 5, 5, 6, 7, 7, 8, 4, 4, 5, 2, 2, 3)
bloco = rep(c("B1","B2","B3","B4","B5"),3)
dbc.ad(doses, bloco, resp, responsead=rnorm(3,6,0.1),grau=2)

Descriptive: Descriptive analysis

Description

Performs the descriptive analysis of an experiment with a factor of interest.

Usage

desc(trat, response, ylab = "Response", xlab = "Treatment", ylim = NA)

Arguments

Value

The function returns exploratory measures of position and dispersion, such as mean, median, maximum, minimum, coefficient of variation, etc ...

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

desc2fat, tabledesc,dispvar

Examples

library(AgroR)
data("pomegranate")
with(pomegranate, desc(trat,WL))

Descriptive: Descriptive analysis (Two factors)

Description

It performs the descriptive analysis of an experiment with two factors of interest.

Usage

desc2fat(
  f1,
  f2,
  response,
  ylab = "Response",
  xlab = c("F1", "F2"),
  theme = theme_classic()
)

Arguments

Value

The function returns exploratory measures of position and dispersion, such as mean, median, maximum, minimum, coefficient of variation, etc ...

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data(cloro)
output=with(cloro, desc2fat(f1,f2,resp))
output$plot_single
output$plot_interaction

Descriptive: Descriptive analysis (Three factors)

Description

Performs the descriptive graphical analysis of an experiment with three factors of interest.

Usage

desc3fat(
  f1,
  f2,
  f3,
  response,
  legend.title = "Legend",
  xlab = c("F1", "F2", "F3"),
  ylab = "Response",
  theme = theme_classic(),
  plot = "interaction"
)

Arguments

Value

The function returns a triple interaction graph.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data(enxofre)
with(enxofre, desc3fat(f1, f2, f3, resp))

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

Description

Regression analysis by orthogonal polynomials for double factorial scheme with additional control. Cases in which the additional belongs to the regression curve, being common to the qualitative levels. In these cases, the additional (usually dose 0/control treatment) is not part of the factor arrangement. One option addressed by this function is to analyze a priori as a double factorial scheme with an additional one and correct the information a posteriore using information from the initial analysis, such as the degree of freedom and the sum of squares of the residue.

Usage

desd_fat2_quant_ad(output, ad.value = 0, design = "FAT2DIC.ad", grau = 1)

Arguments

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

#==================================================
# Data set
trat=rep(c("A","B","C"),e=12)
dose=rep(rep(c(200,400,600,800),e=3),3)
d0=c(40,45,48)
respo=c(60,55,56, 60,65,66, 70,75,76,
        80,85,86, 50,55,56, 70,75,76,
        60,65,66, 50,45,46, 50,45,46,
        50,55,66, 70,75,76, 80,85,86)
repe=rep(c("R1","R2","R3"),12)
#==================================================
# Analysis FAT2DIC.ad
resu=FAT2DIC.ad(trat,dose,repe = repe,respo,responseAd = d0,quali = c(TRUE,FALSE),grau21 = c(1,2,1))

#==================================================
# Regression analysis
desd_fat2_quant_ad(resu,ad.value=0,design="FAT2DIC.ad")


# Data set
trat=rep(c("A","B"),e=12)
dose=rep(rep(c(200,400,600,800),e=3),2)
d0=c(40,45,48)
respo=c(60,55,56,60,65,66,70,75,76,80,85,86,50,45,46,50,55,66,70,75,76,80,85,86)
repe=rep(c("R1","R2","R3"),8)
#==================================================
# Analysis FAT2DIC.ad
resu=FAT2DIC.ad(trat,dose,repe = repe,respo,responseAd = d0,quali = c(TRUE,FALSE))
#==================================================
# Regression analysis
desd_fat2_quant_ad(resu,ad.value=0,design="FAT2DIC.ad",grau=1)

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

Description

Statistical analysis of experiments conducted in a completely randomized with an additional treatment and balanced design with a factor considering the fixed model.

Usage

dic.ad(
  trat,
  response,
  responsead,
  grau = 1,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  theme = theme_classic(),
  ylab = "response",
  xlab = "independent",
  family = "sans",
  posi = "top",
  pointsize = 4.5,
  linesize = 0.8,
  width.bar = NA,
  point = "mean_sd"
)

Arguments

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, adjustment of regression models up to grade 3 polynomial. The function also returns a standardized residual plot.

Note

In some experiments, the researcher may study a quantitative factor, such as fertilizer doses, and present a control, such as a reference fertilizer, treated as a qualitative control. In these cases, there is a difference between considering only the residue in the unfolding of the polynomial, removing or not the qualitative treatment, or since a treatment is excluded from the analysis. In this approach, the residue used is also considering the qualitative treatment, a method similar to the factorial scheme with additional control.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

datadicad=data.frame(doses = c(rep(c(1:5),e=3)),
                     resp = c(3,4,3,5,5,6,7,7,8,4,4,5,2,2,3))
with(datadicad,dic.ad(doses, resp, rnorm(3,6,0.1),grau=2))

Descriptive: Boxplot with standardized data

Description

It makes a graph with the variables and/or treatments with the standardized data.

Usage

dispvar(
  data,
  trat = NULL,
  theme = theme_bw(),
  ylab = "Standard mean",
  xlab = "Variable",
  family = "serif",
  textsize = 12,
  fill = "lightblue"
)

Arguments

Value

Returns a chart of boxes with standardized data

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data("pomegranate")
dispvar(pomegranate[,-1])
trat=pomegranate$trat
dispvar(pomegranate[,-1], trat)

Analysis: Post-hoc Dunn

Description

Perform Kruskal wallis and dunn post-hoc test

Usage

dunn(trat, resp, method = "holm", alpha = 0.05, decreasing = TRUE)

Arguments

Value

Kruskal-wallis and dunn's post-hoc test returns

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Examples

library(AgroR)
data(pomegranate)

with(pomegranate, dunn(trat, WL))

Analysis: Dunnett test

Description

The function performs the Dunnett test

Usage

dunnett(
  trat,
  resp,
  control,
  model = "DIC",
  block = NA,
  column = NA,
  line = NA,
  alpha.t = 0.05,
  pointsize = 5,
  pointshape = 21,
  linesize = 1,
  labelsize = 4,
  textsize = 12,
  errorsize = 1,
  widthsize = 0.2,
  label = "Response",
  family = "sans"
)

Arguments

Value

I return the Dunnett test for experiments in a completely randomized design, randomized blocks or Latin square.

Note

Do not use the "-" symbol or space in treatment names

Examples

#====================================================
# complete randomized design
#====================================================
data("pomegranate")
with(pomegranate,dunnett(trat=trat,resp=WL,control="T1"))

#====================================================
# randomized block design in factorial double
#====================================================
library(AgroR)
data(cloro)
attach(cloro)
respAd=c(268, 322, 275, 350, 320)
a=FAT2DBC.ad(f1, f2, bloco, resp, respAd,
             ylab="Number of nodules", mcomp="sk")
data=rbind(data.frame(trat=paste(f1,f2,sep = ""),bloco=bloco,resp=resp),
           data.frame(trat=c("Test","Test","Test","Test","Test"),
                      bloco=unique(bloco),resp=respAd))
with(data,dunnett(trat = trat,
                  resp = resp,
                  control = "Test",
                  block=bloco,model = "DBC"))

Dataset: Emergence of passion fruit seeds over time .

Description

The data come from an experiment conducted at the State University of Londrina, aiming to study the emergence of yellow passion fruit seeds over time. Data are partial from one of the treatments studied. Four replicates with eight seeds each were used.

Usage

data("emerg")

Format

data.frame containing data set

time

numeric vector with time

resp

Numeric vector with emergence

See Also

aristolochia, cloro, laranja, enxofre, laranja, mirtilo, passiflora, phao, porco, pomegranate, simulate1, simulate2, simulate3, tomate, weather

Examples

data(emerg)

Dataset: Sulfur data

Description

The experiment was carried out in a randomized block design in a 3 x 3 x 3 triple factorial scheme: syrup volume (75, 225 and 675 L), sulfur doses (150, 450, 1350) and time of application (vegetative, complete cycle and reproductive system) with four repetitions. Yield in kg / ha of soybean was evaluated.

Usage

data(enxofre)

Format

data.frame containing data set

f1

Categorical vector with factor 1

f2

Categorical vector with factor 2

f2

Categorical vector with factor 3

bloco

Categorical vector with block

resp

Numeric vector

See Also

cloro, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(enxofre)

Dataset: Eucaliptus grandis Barbin (2013)

Description

The data refer to the height in meters of *Eucalyptus grandis* plants, with 7 years of age, from three trials (Araraquara - Exp 1; Bento Quintino - Exp 2; Mogi-Guacu - Exp 3) in randomized blocks, under 6 progenies. The data were taken from the book by Decio Barbin (2013) and are from the Instituto Florestal de Tupi/SP.

Usage

data("eucalyptus")

Format

data.frame containing data set

trati

Categorical vector with treatments

bloc

Categorical vector with block

exp

Categorical vector with experiment

resp

Numeric vector

References

Planejamento e Analise Estatistica de Experimentos Agronomicos (2013) - Decio Barbin - pg. 177

See Also

cloro, enxofre, laranja, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather

Examples

data(eucalyptus)

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

Description

Summarizes the output returned in the summarise_anova function in list form. The advantage is that the table, in the case of significant interaction, is returned in a format that facilitates assembly in terms of scientific publication.

Usage

fat2_table(output, nf1, nf2, column = 1)

Arguments

Value

returns a list containing analysis output for experiments in FAT2DIC, FAT2DIC.ad, FAT2DBC, FAT2DBC.ad, PSUBDIC and PSUBDBC design.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Examples

#==============================================================
data(corn)
attach(corn)
a=FAT2DIC(A, B, Resp, quali=c(TRUE, TRUE))
output_1=summarise_anova(list(a),design="FAT2DIC",divisor = FALSE)
fat2_table(output_1,nf1=3,nf2=2,column=1)

#==============================================================
data(cloro)
respAd=c(268, 322, 275, 350, 320)
resu=with(cloro, FAT2DIC.ad(f1, f2, bloco, resp, respAd))
output_2=summarise_anova(list(resu),design="FAT2DIC.ad",divisor = FALSE)
fat2_table(output_2,nf1=2,nf2=4,column=1)

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

Description

group graphs of the output of simple experiments into dic, dbc or dql. It is possible to group up to 6 graphs in different arrangements (see model argument)

Usage

grid.onefactor(output, model = "type1")

Arguments

Details

- 'type1': Two graphs next to each other - 'type2': Two graphs one below the other - 'type3': Three graphs, two top and one centered below - 'type4': Three graphs one below the other - 'type5': Four graphs, two at the top and two at the bottom - 'type6': Four graphs one below the other - 'type7': Five graphs, two at the top, two in the middle and one centered at the bottom - 'type8': Five graphs, three at the top, two centered at the bottom - 'type9': Six graphs, three at the top, three centered at the bottom - 'type10': Six graphs, two at the top, two in the middle and two at the bottom

Value

returns grouped graphs

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("pomegranate")
attach(pomegranate)
a=DIC(trat, WL, geom = "point", ylab = "WL")
b=DIC(trat, SS, geom = "point", ylab="SS")
c=DIC(trat, AT, geom = "point", ylab = "AT")
grid.onefactor(list(a,b),model = "type1")
grid.onefactor(list(a,b),model = "type2")
grid.onefactor(list(a,b,c),model = "type3")
grid.onefactor(list(a,b,c),model = "type4")

Graph: Invert letters for two factor chart

Description

invert uppercase and lowercase letters in graph for factorial scheme the subdivided plot with significant interaction

Usage

ibarplot.double(analysis)

Arguments

Value

Return column chart for two factors

Examples

data(covercrops)
attach(covercrops)
a=FAT2DBC(A, B, Bloco, Resp, ylab=expression("Yield"~(Kg~"100 m"^2)),
legend = "Cover crops",alpha.f = 0.3,family = "serif")
ibarplot.double(a)

Analysis: Method to evaluate similarity of experiments based on QMres

Description

This function presents a method to evaluate similarity of experiments based on a matrix of QMres of all against all. This is used as a measure of similarity and applied in clustering.

Usage

jointcluster(qmres, information = "matrix", method.cluster = "ward.D")

Arguments

Value

Returns a residual mean square ratio matrix, bar graph with ratios sorted in ascending order, or cluster analysis.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Examples

qmres=c(0.344429, 0.300542, 0.124833, 0.04531, 0.039571, 0.011812, 0.00519)
jointcluster(qmres,information = "cluster")
jointcluster(qmres,information = "matrix")
jointcluster(qmres,information = "bar")

data(mirtilo)
m=lapply(unique(mirtilo$exp),function(x){
  m=with(mirtilo[mirtilo$exp==x,],DBC(trat,bloco,resp))})
jointcluster(m)

Dataset: Orange plants under different rootstocks

Description

An experiment was conducted with the objective of studying the behavior of nine rootstocks for the Valencia orange tree. The data set refers to the 1973 evaluation (12 years old). The rootstocks are: T1: Tangerine Sunki; T2: National rough lemon; T3: Florida rough lemon; T4: Cleopatra tangerine; T5: Citranger-troyer; T6: Trifoliata; T7: Clove Tangerine; T8: Country orange; T9: Clove Lemon. The number of fruits per plant was evaluated.

Usage

data(laranja)

Format

data.frame containing data set

f1

Categorical vector with treatments

bloco

Categorical vector with block

resp

Numeric vector with number of fruits per plant

References

Planejamento e Analise Estatistica de Experimentos Agronomicos (2013) - Decio Barbin - pg. 72

See Also

cloro, enxofre, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(laranja)

Graph: Line chart

Description

Performs a descriptive line graph with standard deviation bars

Usage

line_plot(
  time,
  response,
  factor = NA,
  errorbar = "sd",
  ylab = "Response",
  xlab = "Time",
  legend.position = "right",
  theme = theme_classic()
)

Arguments

Value

Returns a line chart with error bars

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

sk_graph, plot_TH, corgraph, spider_graph

Examples

dose=rep(c(0,2,4,6,8,10),e=3,2)
resp=c(seq(1,18,1),seq(2,19,1))
fator=rep(c("A","B"),e=18)
line_plot(dose,resp,fator)

Analysis: Logistic regression

Description

Logistic regression is a very popular analysis in agrarian sciences, such as in fruit growth curves, seed germination, etc...The logistic function performs the analysis using 3 or 4 parameters of the logistic model, being imported from the LL function .3 or LL.4 of the drc package (Ritz & Ritz, 2016).

Usage

logistic(
  trat,
  resp,
  npar = "LL.3",
  error = "SE",
  ylab = "Dependent",
  xlab = expression("Independent"),
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  font.family = "sans"
)

Arguments

Details

The three-parameter log-logistic function with lower limit 0 is

f(x) = 0 + \frac{d}{1+\exp(b(\log(x)-\log(e)))}

The four-parameter log-logistic function is given by the expression

f(x) = c + \frac{d-c}{1+\exp(b(\log(x)-\log(e)))}

The function is symmetric about the inflection point (e).

Value

The function allows the automatic graph and equation construction of the logistic model, provides important statistics, such as the Akaike (AIC) and Bayesian (BIC) inference criteria, coefficient of determination (r2), square root of the mean error ( RMSE).

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley and Sons (p. 330).

Ritz, C.; Strebig, J.C.; Ritz, M.C. Package ‘drc’. Creative Commons: Mountain View, CA, USA, 2016.

Examples

data("emerg")
with(emerg, logistic(time, resp,xlab="Time (days)",ylab="Emergence (%)"))
with(emerg, logistic(time, resp,npar="LL.4",xlab="Time (days)",ylab="Emergence (%)"))

Dataset: Cutting blueberry data

Description

An experiment was carried out in order to evaluate the rooting (resp1) of blueberry cuttings as a function of the cutting size (Treatment Colume). This experiment was repeated three times (Location column) and a randomized block design with four replications was adopted.

Usage

data(mirtilo)

Format

data.frame containing data set

trat

Categorical vector with treatments

exp

Categorical vector with experiment

bloco

Categorical vector with block

resp

Numeric vector

See Also

cloro, enxofre, laranja, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather

Examples

data(mirtilo)
attach(mirtilo)

Dataset: Orchard

Description

An experiment was carried out to analyze the treatments in orchards applied in the rows and between the rows, in a split-plot scheme according to a randomized block design. For this case, the line and leading are considered the levels of the factor applied in the plots and the treatments are considered the levels of the factor applied in the subplots. Microbial biomass carbon was analyzed.

Usage

data(orchard)

Format

data.frame containing data set

A

Categorical vector with plot

B

Categorical vector with split-plot

Bloco

Categorical vector with block

Resp

Numeric vector with microbial biomass carbon

See Also

enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(orchard)

Dataset: Substrate data in the production of passion fruit seedlings

Description

An experiment was carried out in order to evaluate the influence of the substrate on the dry mass of aerial part and root in yellow sour passion fruit. The experiment was conducted in a randomized block design with four replications. The treatments consisted of five substrates (Vermiculite, MC Normal, Carolina Soil, Mc organic and sand)

Usage

data(passiflora)

Format

data.frame containing data set

trat

Categorical vector with substrate

bloco

Categorical vector with block

MSPA

Numeric vector with dry mass of aerial part

MSR

Numeric vector with dry mass of root

See Also

cloro, enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather

Examples

data(passiflora)

Dataset: Pepper

Description

A vegetable breeder is characterizing five mini pepper accessions from the State University of Londrina germplasm bank for agronomic and biochemical variables. The experiment was conducted in a completely randomized design with four replications

Usage

data(pepper)

Format

data.frame containing data set

Acesso

Categorical vector with accessions

MS

Numeric vector com dry mass

VitC

Numeric vector with Vitamin C

See Also

enxofre, laranja, mirtilo, pomegranate, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather, phao, passiflora, aristolochia

Examples

data(pepper)

Dataset: Osmocote in Phalaenopsis sp.

Description

The objective of the work was to evaluate the effect of doses of osmocote (15-09-12-N-P2O5-K2O, respectively) on the initial development of the orchid Phalaenopsis sp. The osmocote fertilizer was added in the following doses: 0, 2, 4, 6 and 8 g vase-1. After twelve months, leaf length was evaluated.

Usage

data(phao)

Format

data.frame containing data set

dose

Numeric vector with doses

comp

Numeric vector with leaf length

References

de Paula, J. C. B., Junior, W. A. R., Shimizu, G. D., Men, G. B., & de Faria, R. T. (2020). Fertilizante de liberacao controlada no crescimento inicial da orquidea Phalaenopsis sp. Revista Cultura Agronomica, 29(2), 289-299.

See Also

pomegranate, passiflora, cloro, enxofre, laranja, mirtilo, porco, sensorial, simulate1, simulate2, simulate3, tomate, weather

Examples

data(phao)

Graph: Climate chart of temperature and humidity

Description

The plot_TH function allows the user to build a column/line graph with climatic parameters of temperature (maximum, minimum and average) and relative humidity (UR) or precipitation. This chart is widely used in scientific work in agrarian science

Usage

plot_TH(
  tempo,
  Tmed,
  Tmax,
  Tmin,
  UR,
  xlab = "Time",
  yname1 = expression("Humidity (%)"),
  yname2 = expression("Temperature ("^o * "C)"),
  legend.H = "Humidity",
  legend.tmed = "Tmed",
  legend.tmin = "Tmin",
  legend.tmax = "Tmax",
  colormax = "red",
  colormin = "blue",
  colormean = "darkgreen",
  fillbar = "gray80",
  limitsy1 = c(0, 100),
  x = "days",
  breaks = "1 months",
  textsize = 12,
  legendsize = 12,
  titlesize = 12,
  linesize = 1,
  date_format = "%m-%Y",
  sc = 2.5,
  angle = 0,
  legend.position = "bottom",
  theme = theme_classic()
)

Arguments

Value

Returns row and column graphs for graphical representation of air temperature and relative humidity. Graph normally used in scientific articles

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

sk_graph, barplot_positive, corgraph, plot_TH1, spider_graph, line_plot

Examples

library(AgroR)
data(weather)
with(weather, plot_TH(tempo, Tmed, Tmax, Tmin, UR))

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

Description

The plot_TH1 function allows the user to build a column/line graph with climatic parameters of temperature (maximum, minimum and average) and relative humidity (UR) or precipitation. This chart is widely used in scientific work in agrarian science

Usage

plot_TH1(
  tempo,
  Tmed,
  Tmax,
  Tmin,
  UR,
  xlab = "Time",
  yname1 = expression("Humidity (%)"),
  yname2 = expression("Temperature ("^o * "C)"),
  legend.T = "Temperature",
  legend.H = "Humidity",
  legend.tmed = "Tmed",
  legend.tmin = "Tmin",
  legend.tmax = "Tmax",
  colormax = "red",
  colormin = "blue",
  colormean = "darkgreen",
  fillarea = "darkblue",
  facet.fill = "#FF9933",
  panel.grid = FALSE,
  x = "days",
  breaks = "1 months",
  textsize = 12,
  legendsize = 12,
  titlesize = 12,
  linesize = 1,
  date_format = "%m-%Y",
  angle = 0,
  legend.position = c(0.1, 0.3)
)

Arguments

Value

Returns row and column graphs for graphical representation of air temperature and relative humidity. Graph normally used in scientific articles

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

sk_graph, barplot_positive, corgraph, spider_graph, line_plot

Examples

library(AgroR)
data(weather)
with(weather, plot_TH1(tempo, Tmed, Tmax, Tmin, UR))

Graph: Plot correlation

Description

Correlation analysis function (Pearson or Spearman)

Usage

plot_cor(
  x,
  y,
  method = "pearson",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  pointsize = 5,
  shape = 21,
  fill = "gray",
  color = "black",
  axis.size = 12,
  ic = TRUE,
  title = NA,
  family = "sans"
)

Arguments

Value

The function returns a graph for correlation

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("pomegranate")
with(pomegranate, plot_cor(WL, SS, xlab="WL", ylab="SS"))

Graph: Interaction plot

Description

Performs an interaction graph from an output of the FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC commands.

Usage

plot_interaction(
  a,
  box_label = TRUE,
  repel = FALSE,
  pointsize = 3,
  linesize = 0.8,
  width.bar = 0.05,
  add.errorbar = TRUE,
  family = "sans"
)

Arguments

Value

Returns an interaction graph with averages and letters from the multiple comparison test

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data(cloro)
a=with(cloro, FAT2DIC(f1, f2, resp))
plot_interaction(a)

Graph: Column, box or segment chart with observations

Description

The function performs the construction of graphs of boxes, columns or segments with all the observations represented in the graph.

Usage

plot_jitter(model)

Arguments

Value

Returns with graph of boxes, columns or segments with all the observations represented in the graph.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data("pomegranate")
a=with(pomegranate,DIC(trat,WL,geom="point"))
plot_jitter(a)

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

Description

Sometimes the researcher wants to test whether the treatment mean is greater than/equal to or less than a reference value. For example, I want to know if the average productivity of my treatment is higher than the average productivity of a given country. For this, this function allows comparing the means with a reference value using the t test.

Usage

plot_tonetest(tonetest, alpha = 0.95)

Arguments

Value

returns a density plot and a column plot to compare a reference value with other treatments.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Examples

library(AgroR)
data("pomegranate")
resu=tonetest(resp=pomegranate$WL, trat=pomegranate$trat, mu=2)
plot_tonetest(resu)

Analysis: Linear regression graph

Description

Linear regression analysis of an experiment with a quantitative factor or isolated effect of a quantitative factor

Usage

polynomial(
  trat,
  resp,
  ylab = "Response",
  xlab = "Independent",
  yname.poly = "y",
  xname.poly = "x",
  grau = NA,
  theme = theme_classic(),
  point = "mean_sd",
  color = "gray80",
  posi = "top",
  textsize = 12,
  se = FALSE,
  ylim = NA,
  family = "sans",
  pointsize = 4.5,
  linesize = 0.8,
  width.bar = NA,
  n = NA,
  SSq = NA,
  DFres = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

Returns linear, quadratic or cubic regression analysis.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

polynomial2, polynomial2_color

Examples

data("phao")
with(phao, polynomial(dose,comp, grau = 2))

Analysis: Linear regression graph in double factorial

Description

Linear regression analysis for significant interaction of an experiment with two factors, one quantitative and one qualitative

Usage

polynomial2(
  fator1,
  resp,
  fator2,
  color = NA,
  grau = NA,
  ylab = "Response",
  xlab = "Independent",
  theme = theme_classic(),
  se = FALSE,
  point = "mean_sd",
  legend.title = "Treatments",
  posi = "top",
  textsize = 12,
  ylim = NA,
  family = "sans",
  width.bar = NA,
  pointsize = 3,
  linesize = 0.8,
  separate = c("(\"", "\")"),
  n = NA,
  DFres = NA,
  SSq = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments

Value

Returns two or more linear, quadratic or cubic regression analyzes.

Author(s)

Gabriel Danilo Shimizu, gabrield.shimizu@gmail.com

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

polynomial, polynomial2_color

Examples

dose=rep(c(0,0,0,2,2,2,4,4,4,6,6,6),3)
resp=c(8,7,5,23,24,25,30,34,36,80,90,80,
12,14,15,23,24,25,50,54,56,80,90,40,
12,14,15,3,4,5,50,54,56,80,90,40)
trat=rep(c("A","B","C"),e=12)
polynomial2(dose, resp, trat, grau=c(1,2,3))

Analysis: Linear regression graph in double factorial with color graph

Description

Linear regression analysis for significant interaction of an experiment with two factors, one quantitative and one qualitative

Usage

polynomial2_color(
  fator1,
  resp,
  fator2,
  color = NA,
  grau = NA,
  ylab = "Response",
  xlab = "independent",
  theme = theme_classic(),
  se = FALSE,
  point = "mean_se",
  legend.title = "Treatments",
  posi = "top",
  textsize = 12,
  ylim = NA,
  family = "sans",
  width.bar = NA,
  pointsize = 5,
  linesize = 0.8,
  separate = c("(\"", "\")"),
  n = NA,
  DFres = NA,
  SSq = NA,
  print.on = TRUE,
  plot.on = TRUE
)

Arguments