Package 'AgroR'

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, [email protected]

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)

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)

Description

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

Usage

bar_dunnett(
  output.dunnett,
  ylab = "Response",
  xlab = "",
  fill = c("#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",
             legend = "Stages",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)

Description

This is a function of the bar graph for one factor

Usage

bar_graph(
  model,
  fill = "lightblue",
  horiz = TRUE,
  width.col = 0.9,
  axis.0 = FALSE
)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

radargraph, 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)

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.col = 0.9,
  width.bar = 0,
  color.info = "black",
  fill = "lightblue"
)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

radargraph, 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')

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,
  width.bar = 0.15,
  facet.background = "gray80"
)

Arguments

Value

Returns a bar chart for one factor

Author(s)

Gabriel Danilo Shimizu, [email protected]

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"))

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,
  width.col = 0.9,
  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)

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)

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, [email protected]

See Also

radargraph, 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)")

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)

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)

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))

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, [email protected]

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))

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, [email protected]

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))

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, [email protected]

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))

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, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

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

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, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

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

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)

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)

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 = NULL,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA
)

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, [email protected]

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=expression("Grain yield"~(kg~ha^-1))))

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, [email protected]

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)

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, [email protected]

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")

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, [email protected]

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")

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, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

See Also

desc2fat, tabledesc,dispvar

Examples

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

Description

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

Usage

desc2fat(f1, f2, response, ylab = "Response", 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, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

library(AgroR)
data(cloro)
with(cloro, desc2fat(f1,f2,resp))

Description

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

Usage

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

Arguments

Value

The function returns a triple interaction graph.

Author(s)

Gabriel Danilo Shimizu, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

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

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, [email protected]

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)

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 = NULL,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA
)

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, [email protected]

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 = expression("Dose"~(g~vase^-1)),
               ylab="Leaf length (cm)"))

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

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, [email protected]

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))

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, [email protected]

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")

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, [email protected]

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))

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, [email protected]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

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

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 = NULL,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA
)

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, [email protected]

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)"))

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, [email protected]

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)

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, [email protected]

Examples

library(AgroR)
data(pomegranate)

with(pomegranate, dunn(trat, WL))

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",
  fontfamily = "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",
             legend = "Stages",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"))

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)

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)

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)

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

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)

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",
  xlab = "",
  xlab.factor = c("F1", "F2"),
  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
)

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, [email protected]

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

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",
  xlab = "",
  xlab.factor = c("F1", "F2"),
  legend = "Legend",
  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
)

Arguments