Type:	Package
Title:	Analysis of Experimental Data using ANOVA and Mean Comparison
Version:	0.1.0
Description:	Provides tools for designing and analyzing agricultural experiments. It includes functions for generating randomized treatment layouts for standard experimental designs such as Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Randomized Block Design (FRBD), split-plot design, and strip-plot design. The package implements one-factor and two-factor analysis of variance (ANOVA) and offers multiple comparison procedures, including Least Significant Difference (LSD), Tukey, and Duncan tests, to compare treatment means in single-factor and factorial experiments. The methods follow classical experimental design principles described in Gomez and Gomez (1984, Statistical Procedures for Agricultural Research, John Wiley & Sons, New York).
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
Imports:	ggplot2, grid, multcompView, stats, utils
RoxygenNote:	7.3.3
Depends:	R (≥ 3.5)
NeedsCompilation:	no
Packaged:	2026-04-24 11:26:49 UTC; HPC
Author:	Santosh Patil [aut, cre], Yogesh Garde [aut]
Maintainer:	Santosh Patil <patil.sgstat@gmail.com>
Repository:	CRAN
Date/Publication:	2026-04-28 19:00:43 UTC

Groundnut Dataset

Description

Hypothetical dataset for single-factor groundnut experiment.

Usage

Gnutdataset

Format

A data frame with 24 observations and 10 variables:

Trt

Treatment levels

Rep

Replication

Germ

Germination percentage

Plntht

Plant height (cm)

Noflwr

Number of flowers per plant

Spread

Plant spread (cm)

Leaflnth

Leaf length (cm)

Nopodplnt

Number of pods per plant

Wt

Pod weight per plant (g)

Disease

Disease incidence

Details

Used for demonstration of one-factor ANOVA.

Source

Simulated data

Rice Dataset

Description

Hypothetical dataset for two-factor experiment (treatment and spacing).

Usage

Ricedataset

Format

A data frame with 96 observations and 11 variables:

TRT

Treatment

SPACE

Spacing

BLOCK

Block

DFF

Days to flowering

PH

Plant height

TTP

Tillers per plant

PL

Panicle length

GPP

Grains per panicle

HSW

Hundred seed weight

GYP

Grain yield

Disease

Disease incidence

Details

Used for two-factor ANOVA demonstration.

Source

Simulated data

Soil Dataset

Description

Hypothetical dataset for single-factor soil experiment.

Usage

Soildataset

Format

A data frame with 33 observations and 15 variables:

TRT

Treatment

REP

Replication

BD

Bulk density

PD

Particle density

POR

Porosity

HC

Hydraulic conductivity

pH

Soil pH

CEC

Cation exchange capacity

N

Nitrogen

P

Phosphorus

K

Potassium

S

Sulfur

OC

Organic carbon

MBN

Microbial biomass nitrogen

MBC

Microbial biomass carbon

Details

Used for soil analysis demonstration.

Source

Simulated data

One-Factor ANOVA for Agricultural Designs

Description

Performs analysis of variance for one-factor experiments under Completely Randomized Design (CRD), Randomized Block Design (RBD), and Latin Square Design (LSD). Supports multiple response variables, data transformation, and mean comparison tests.

Usage

aov_of(
  data,
  design = c("CRD", "RBD", "LSD"),
  treatment,
  replication = NULL,
  row = NULL,
  column = NULL,
  responsevar = "all",
  test = c("lsd", "tukey", "duncan"),
  transform = NULL,
  narc = 1,
  alpha = 0.05
)

Arguments

Details

This function supports:

• Analysis for CRD, RBD, and LSD designs

• Multiple response variables

• Data transformations (log, square root, arcsine)

• Mean separation tests (LSD, Tukey, Duncan)

• Combined summary table output

Value

A list containing:

Individual outputs

Analysis results for each response variable separately.

Summary statistics

Arithmetic mean, standard deviation, minimum, and maximum values for each variable.

Shapiro-Wilk test

Results of the Shapiro-Wilk test for normality.

ANOVA and mean comparison

ANOVA table along with mean comparison based on the selected test for each response variable.

Combined table

A summary table including mean ± SD, grouping letters, SEm, SEd, CD, and CV values for all variables.

Examples

# Example
data(Gnutdataset)
head(Gnutdataset)
Out_rbd <- aov_of(
           Gnutdataset,
           design = "RBD",
           treatment = "Trt",
           replication = "Rep",
           responsevar = "all",
           transform = list(Noflwr="sqrt", Germ="arcsine", Disease="arcsine"),
           narc = list(Germ=10, Disease=5),
           alpha = 0.05 )

# Single variable result
Out_rbd$Germ

# Combined table for all varaibles
Out_rbd$Combined_table

# Entire output
Out_rbd

#Example without transformation
Out_rbd2 <- aov_of(
  Soildataset,
  design = "RBD",
  treatment = "TRT",
  replication = "REP",
  responsevar = "all",
  test = "lsd",
  alpha = 0.05
)

Out_rbd2$Combined_table

Two-Factor ANOVA for Agricultural Experimental Designs

Description

Performs analysis of variance for two-factor experiments Factorial CRD (FCRD), Factorial RBD (FRBD), Split-Plot, and Strip-Plot designs.

Usage

aov_tf(
  data,
  design = c("FCRD", "FRBD", "SPLIT", "STRIP"),
  factor_A,
  factor_B,
  replication = NULL,
  responsevar = "all",
  test = c("lsd", "tukey", "duncan"),
  transform = NULL,
  narc = 1,
  alpha = 0.05
)

Arguments

Value

A list containing:

Individual outputs

Analysis results for each response variable separately.

Summary statistics

Arithmetic mean, standard deviation, minimum, and maximum values for each variable.

Shapiro-Wilk test

Results of the Shapiro-Wilk test for normality.

ANOVA and mean comparison

ANOVA table along with mean comparison based on the selected test for each response variable.

Combined table

A summary table -two way including mean ± SD, grouping letters, SEm, SEd, CD, and CV values for all variables.

#'

Examples

#Rice dataset with Factor A as TRT and Factor B as SPACE and replications in "BLOCK" column
data("Gnutdataset")
"head(Ricedataset)"

#Example: Factorial CRD
out <- aov_tf(
  Ricedataset,
  design = "FCRD",
  factor_A = "TRT",
  factor_B = "SPACE",
  replication = "BLOCK",
  responsevar = "all",
  test = "lsd",
  transform = NULL,
  narc = 1,
  alpha = 0.05
)

#Example: Split-plot with transformation
out2 <- aov_tf(
  Ricedataset,
  design = "SPLIT",
  factor_A = "TRT",
  factor_B = "SPACE",
  replication = "BLOCK",
  responsevar = "all",
  test = "lsd",
  transform = list(GPP = "log", Disease = "arcsine"),
  narc = list(Disease = 5),
  alpha = 0.05
)

out2$PH
out2$GPP

Field Design Layout Plot for Agricultural Experiments

Description

Generates randomized field layouts for agricultural experimental designs including Completely Randomized Design (CRD), Randomized Block Design (RBD), and Latin Square Design (LSD), factorial designs, split-plot, and strip-plot.

Usage

fld_layout(
  design = "RBD",
  treatments = paste0("T", 1:5),
  A = c("A1", "A2"),
  B = c("B1", "B2", "B3"),
  blocks = 3,
  replications = 3,
  plot_length = 2,
  plot_width = 1,
  alley = 0.3,
  block_gap = 0.6,
  border = NULL,
  treatment_label_size = NULL,
  block_label_size = NULL,
  show_field_dimensions = TRUE,
  show_plot_dimensions = TRUE,
  seed = NULL
)

Arguments

Details

This function supports:

• Completely Randomized Design (CRD)

• Randomized Block Design (RBD)

• Latin Square Design (LSD)

• Factorial CRD and RBD (FCRD, FRBD)

• Split-plot and strip-plot designs

• Unequal and equal replication structures in CRD

• Custom plot dimensions and spacing

Value

A list containing:

plot

A ggplot object showing field layout

layout

Data frame containing plot arrangement

Examples

layout_crd <- fld_layout(
    design = "CRD",
    treatments = paste0("T", 1:7),
    replications = c(2,3,4,2,1,5,4),
    plot_length = 2.5,
  plot_width = 1.8,
  alley = 0.3,
  border = 1.5,
  seed = 123
  )
  layout_crd$plot


# RBD example
layout_rbd <- fld_layout(
  design = "RBD",
  treatments = paste0("T",1:7),
  blocks = 4,
  seed = 123
)
layout_rbd$plot

layout_rbd <- fld_layout(
  design = "RBD",
  treatments = paste0("T",1:7),
  blocks = 4,
   treatment_label_size = 4,
   block_label_size = 3,
  show_field_dimensions = FALSE,
  show_plot_dimensions = FALSE,
  seed = 123
)
layout_rbd$plot

# Latin Square Design
layout_lsd <- fld_layout(
  design = "LSD",
  treatments = paste0("T", 1:6),
  seed = 123
)
layout_lsd$plot

# Factorial CRD
result <- fld_layout(
  design = "FCRD",
  A = c("A1", "A2", "A3"),
  B = c("B1", "B2", "B3", "B4"),
  replications = 3,
  seed = 101
)
result$plot

# Split plot
result2 <- fld_layout(
  design = "SPLIT",
  A = c("A1", "A2", "A3"),
  B = c("B1", "B2", "B3", "B4"),
  blocks = 3,
  seed = 101 )
result2$plot