Type: Package
Title: Comprehensive Statistical Analysis of Plant Breeding Experiments
Version: 0.4.4
Maintainer: Nandan Patil <tryanother609@gmail.com>
Note: Department of Genetics and Plant Breeding, University of Agricultural Sciecnes, Dharwad.
Description: Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) http://repository.ias.ac.in/89299/ and Diallel analysis as per Griffing, B. (1956) https://www.publish.csiro.au/bi/pdf/BI9560463.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.5.0)
Date: 2024-10-12
URL: https://github.com/nandp1/gpbStat/
BugReports: https://github.com/nandp1/gpbStat/issues
RoxygenNote: 7.3.2
Imports: tidyr, purrr, tibble, magrittr, dplyr
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-10-12 07:21:50 UTC; PATIL
Author: Nandan Patil ORCID iD [aut, cre], Lakshmi Gangavati [aut, ctb]
Repository: CRAN
Date/Publication: 2024-10-12 07:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling 'rhs(lhs)'.

Line x Tester data (only Crosses) in Alpha Lattice design.

Description

The Line x Tester data of containing only crosses laid out in Alpha Lattice design.

Usage

data(alphaltc)

Format

A data frame of five variables of 15 crosses derived from five lines and three testers.

replication

four replications

block

five blocks

line

five inbred genotype

tester

three inbred genotype

yield

trait of intrest

See Also

rcbdltc ,alphaltcchk ,rcbdltcchk

Examples

 result = ltc(alphaltc, replication, line, tester, yield, block)

Line x Tester data (Crosses and Checks) in Alpha Lattice

Description

The sample Line x Tester data of containing crosses and checks laid out in Alpha Lattice design. The data is composed of five lines, three testers and three checks.

Usage

data(alphaltcchk)

Format

A dataframe of six variables.

replication

three replications

block

six blocks

line

five lines

tester

three testers

check

three check

yield

trait of intrest

See Also

rcbdltc ,alphaltc ,rcbdltcchk

Examples

result = ltcchk(alphaltcchk, replication, line, tester, check, yield, block)

Line x Tester data (only Crosses) in Alpha Lattice design.

Description

The Line x Tester data of containing only crosses laid out in Alpha Lattice design.

Usage

data(alphaltcmt)

Format

A data frame of 15 crosses derived from five lines and three testers.

replication

four replications

block

five blocks

line

five inbred genotype

tester

three inbred genotype

hsw

hundred seed weight

sh

shelling per cent

gy

grain yield

See Also

rcbdltc ,alphaltcchk ,rcbdltcchk ,rcbdltcmt

Examples

 result = ltcmt(alphaltcmt, replication, line, tester, alphaltcmt[,5:7], block)

Line x Tester data (only Crosses) with single plant observations laid in Alpha Lattice design.

Description

The Line x Tester data containing single plant observations of only crosses laid out in Alpha Lattice design.

Usage

data(alphaltcs)

Format

A data frame of 15 crosses derived from five lines and three testers.

replication

four replications

block

five blocks

line

five inbred genotype

tester

three inbred genotype

obs

four single plant observations

yield

yield as a dependent trait

See Also

rcbdltcs ,alphaltcchk ,rcbdltcchk ,rcbdltcmt

Examples

 result = ltcs(alphaltcs, replication, line, tester, obs, yield, block)

Data of estimating drought tolerance indices without replication

Description

The sample data containing 15 genotypes evaluated under non-stress and stress conditions without replications

Usage

data(datdti)

Format

A dataframe of eight variables.

ENV

two environment

GEN

fifteen genotypes

CL

trait cob length

CG

trait cob girth

NKR

trait number of kernel rows

NKPR

trait number of kernels per row

HSW

trait hundred seed weight

GY

trait grain yield

See Also

datrdti ,alphaltc ,rcbdltc

Examples

result = dti(datdti, environment = ENV, genotype = GEN, datdti[,3:8], ns = 'NS-DWR', st = 'ST-DWR')

Data of estimating drought tolerance indices with replication

Description

The sample data containing 15 genotypes evaluated under non-stress and stress conditions with replications

Usage

data(datrdti)

Format

A dataframe of nine variables.

ENV

two environment

GEN

fifteen genotypes

REP

two replications

CL

trait cob length

CG

trait cob girth

NKR

trait number of kernel rows

NKPR

trait number of kernels per row

HSW

trait hundred seed weight

GY

trait grain yield

See Also

datdti ,alphaltc ,rcbdltc

Examples

result = dti(datrdti, environment = ENV, genotype = GEN, datrdti[,4:9],
                   ns = 'NS-DWR', st = 'ST-DWR')

Analysis of Diallel Method 2 data containing only Crosses laid out in RCBD or Alpha Lattice design.

Description

Analysis of Diallel Method 2 data containing only Crosses laid out in RCBD or Alpha Lattice design.

Usage

dm2(data, rep, parent1, parent2, var, block)

Arguments

data

dataframe containing following variables

rep

replication

parent1

parent 1

parent2

parent 2

var

trait of interest

block

block (for alpha lattice only)

Details

Analyzing the Diallel Method 2 data containing only crosses which are evaluated in RCBD & Alpha lattice design. All the factors are considered as fixed.

Value

Means

Two way mean table.

ANOVA

ANOVA for the given variable.

Coefficient of Variation

Coefficient of Variation of the variable.

Diallel ANOVA

Diallel ANVOA for the given trait.

Genetic Variance

GCA & SCA varaince.

Combining ability effects

Two way table containing Combining ability effects of parents and crosses

Standard Error

Standard Errror for comining ability effects.

Critical Difference

Critical Difference at 5 pecent for combining ability effects.

Note

The blocks are mentioned at end of the function if the experimental design is Alpha Lattice. For RCBD no need mention the blocks.

Author(s)

Nandan Patil tryanother609@gmail.com

References

Griffing, B. (1956) Concept of General and Specific Combining Ability in relation to Diallel Crossing Systems. Australian Journal of Biological Sciences, 9(4), 463-493.

Dabholkar, A. R. (1999). Elements of Bio Metrical Genetics. Concept Publishing Company, New Delhi.

Singh, R. K. and Chaudhary, B. D. (1977). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi.

See Also

ltcchk, ltc

Examples

## Not run: #Diallel Method 2 analysis containing only crosses in RCBD.
library(gpbStat)
data(dm2rcbd)
result1 =  dm2(dm2rcbd, rep, parent1, parent2, DTP)
result1

#Diallel Method 2 analysis containing only crosses in Alpha Lattice
library(gpbStat)
data(dm2alpha)
result2 =  dm2(dm2alpha, replication, parent1, parent2, TW, block)
result2

# Save results to csv file
lapply(result2, function(x) write.table(data.frame(x), 'result2.csv'  , append= T, sep=','))

## End(Not run)

Diallel Method 2 data in Alpha Lattice.

Description

The Diallel Method 2 data laid out in Alpha Lattice Design.

Usage

data(dm2alpha)

Format

A data frame for Diallel analysis Method 2 containing 105 crosses and 15 parents.

replication

two replications

block

twelve blocks

parent1

fifteen inbred genotype

parent2

fifteen inbred genotype

TW

data for test weight

See Also

alphaltcchk ,alphaltc ,rcbdltcchk ,dm2rcbd

Examples

 result2 =  dm2(dm2alpha, replication, parent1, parent2, TW, block)

Diallel Method 2 data in RCBD

Description

The Diallel Method 2 data laid out in Randomized Complete Block Design (RCBD).

Usage

data(rcbdltc)

Format

A data frame for Diallel analysis Method 2 containing four variables of 105 crosses and 15 parents.

rep

four replications

parent1

five inbred genotype

parent2

three inbred genotype

DTP

data for days to pollen shed

See Also

alphaltcchk ,alphaltc ,rcbdltcchk ,dm2alpha

Examples

 result2 =  dm2(dm2rcbd, rep, parent1, parent2, DTP)

Estimation of Drought Tolerance Indices.

Description

Estimation of Drought Tolerance Indices.

Usage

dti(data, environment, genotype, traits, ns, st)

Arguments

data

dataframe containing following variables

environment

column with two levels i.e., non-stress and stress conditions

genotype

genotypes evaluated

traits

trait of interest

ns

name of level indicating evaluation under non-stress (irrigated) conditions

st

name of level indicating evaluation under stress conditions

Details

Estimation various Drought Tolerance Indices of genotypes evaluated under stress and non-stress conditions of both replicated and non-replicated data.

Value

TOL

Stress tolerance.

STI

Stress tolerance index.

SSPI

Stress susceptibility percentage index.

YI

Yield index.

YSI

Yield stability index.

RSI

Relative stress index.

MP

Mean productivity.

GMP

Geometric mean productivity

HM

Harmonic mean.

MRP

Mean relative performance.

PYR

Percent yield Reduction.

PYR

Drought Susceptibility Index.

SSP

Stress Susceptibility Index.

Note

The function can handle both replicated and non-replicated data refer the examples.

Author(s)

Nandan Patil tryanother609@gmail.com

References

Pour‐Aboughadareh, A., Yousefian, M., Moradkhani, H., Moghaddam Vahed, M., Poczai, P., &amp; Siddique, K. H. (2019). ipastic: An online toolkit to estimate plant abiotic stress indices. Applications in Plant Sciences, 7(7). https://doi.org/10.1002/aps3.11278 Sabouri, A., Dadras, A.R., Singh V., Azar, M., Kouchesfahani, A. S., Taslimi, M. and Jalalifar, R. (2022). Screening of rice drought‑tolerantlines by introducing a new composite selection index and competitive with multivariate methods. Scientific Reports, 12. https://doi.org/10.1038/s41598-022-06123-9 Fischer, R. and Maurer, R. (1978) Drought Resistance in Spring Wheat Cultivars. I. Grain Yield Responses. Australian Journal of Agricultural Research, 29, 897-912. https://doi.org/10.1071/AR9780897

See Also

ltc, ltcchk, ltcmt

Examples

## Not run: # Estimating drought tolerance indices
library(gpbStat)

data(datdti)
result1 =  dti(datdti, environment = ENV, genotype = GEN, datdti[,3:8],
              ns = 'NS-DWR', st = 'ST-DWR')
result1

data(datrdti)
result2 = dti(datrdti, environment = ENV, genotype = GEN, datrdti[,4:9],
            ns = 'NS-DWR', st = 'ST-DWR')
result2

## End(Not run)

Analysis of Line x Tester data containing only Crosses laid out in RCBD or Alpha Lattice design.

Description

Analysis of Line x Tester data containing only Crosses laid out in RCBD or Alpha Lattice design.

Usage

ltc(data, replication, line, tester, y, block)

Arguments

data

dataframe containing following variables

replication

replication

line

line

tester

tester

y

trait of interest

block

block (for alpha lattice design only)

Details

Analyzing the line by tester data only using the data from crosses which are evaluated in alpha lattice design. All the factors are considered as fixed.

Value

Overall ANOVA

ANOVA with all the factors.

Coefficient of Variation

ANOVA with all the factors.

Genetic Variance

Phenotypic and Genotypic variance for the given trait.

Genetic Variability

Phenotypic coefficient of variability and Genotypic coefficient of variability and Environmental coefficient of Variation.

Proportional Contribution

Propotional contribution of Lines, Tester and Line x Tester interaction.

GCA lines

Combining ability effects of lines.

GCA testers

Combining ability effects of testers.

SCA crosses

Combining ability effects of crosses

Line x Tester ANOVA

ANOVA with all the factors.

GV Singh & Chaudhary

Genetic component of Variance as per Singh and Chaudhary, 1977.

Standard Errors

Standard error for combining ability effects.

Critical Difference

Critical Difference at 5 pecent for combining ability effects.

Note

The block variable is inserted at the last if the experimental design is Alpha Lattice. For RCBD no need to have block factor.

Author(s)

Nandan Patil tryanother609@gmail.com

References

Kempthorne, O. (1957), Introduction to Genetic Statistics. John Wiley and Sons, New York. , 468-472. Singh, R. K. and Chaudhary, B. D. (1977). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi.

See Also

ltcchk, dm2, ltcmt

Examples

## Not run: #Line Tester analysis data with only crosses in RCBD
library(gpbStat)
data(rcbdltc)
result1 = ltc(rcbdltc, replication, line, tester, yield)
result1

#Line Tester analysis data with only crosses in Alpha Lattice
library(gpbStat)
data(alphaltc)
result2 = ltc(alphaltc, replication, line, tester, yield, block)
result2

## End(Not run)

Analysis of Line x Tester data containing crosses and checks laid out in RCBD or Alpha Lattice experimental design.

Description

Analysis of Line x Tester data containing crosses and checks laid out in RCBD or Alpha Lattice experimental design.

Usage

ltcchk(data, replication, line, tester, check, y, block)

Arguments

data

dataframe containing following variables

replication

replication variable

line

line variable

tester

tester variable

check

check variable

y

trait of interest

block

block variable (for alpha lattice design only)

Details

Analyzing the line by tester data only using the data from crosses which are evaluated in alpha lattice design. All the factors are considered as fixed.

Analyzing the line by tester data only using the data from crosses which are evaluated in alpha lattice design. All the factors are considered as fixed.

Value

Overall ANOVA

ANOVA with all the factors.

Coefficient of Variation

ANOVA with all the factors.

Genetic Variance

Phenotypic and Genotypic variance for the given trait.

Genetic Variability

Phenotypic coefficient of variability and Genotypic coefficient of variability and Environmental coefficient of Variation.

Proportional Contribution

Propotional contribution of Lines, Tester and Line x Tester interaction.

GCA lines

Combining ability effects of lines.

GCA testers

Combining ability effects of testers.

SCA crosses

Combining ability effects of crosses

Line x Tester ANOVA

ANOVA with all the factors.

GV Singh & Chaudhary

Genetic component of Variance as per Singh and Chaudhary, 1977.

Standard Errors

Standard error for combining ability effects.

Critical Difference

Critical Difference at 5 percent for combining ability effects.

Note

The block variable is inserted at the last if the experimental design is Alpha Lattice. For RCBD no need to have block factor.

Author(s)

Nandan Patil

Nandan Patil tryanother609@gmail.com

References

Kempthorne, O. (1957), Introduction to Genetic Statistics. John Wiley and Sons, New York. , 468-472. Singh, R. K. and Chaudhary, B. D. (1977). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi.

See Also

ltc, dm2, ltcmt

Examples

## Not run: #Line x Tester analysis with crosses and checks in RCBD
library(gpbStat)
data(rcbdltcchk)
results = ltcchk(rcbdltcchk, replication, line, tester, check, yield)
results

#Line X Tester analysis with crosses and checks in Alpha Lattice
library(gpbStat)
data(alphaltcchk)
results1 = ltcchk(alphaltcchk, replication, line, tester, check, yield, block)
results1
## End(Not run)

Analysis of Line x Tester data for multiple traits containing only Crosses laid out in RCBD or Alpha Lattice design.

Description

Analysis of Line x Tester data for multiple traits containing only Crosses laid out in RCBD or Alpha Lattice design.

Usage

ltcmt(data, replication, line, tester, traits, block)

Arguments

data

dataframe containing following variables

replication

replication

line

line

tester

tester

traits

multiple traits of interest

block

block (for alpha lattice design only)

Details

Analyzing the line by tester data of multiple trais only using the data from crosses which are evaluated in RCBD and Alpha lattice design. All the factors are considered as fixed.

Value

Mean

Table of means.

ANOVA

ANOVA with all the factors.

GCA.Line

GCA effects of lines.

GCA.Tester

GCA effects of testers.

SCA

SCA effects of crosses.

CV

Coefficent of Variation.

Genetic.Variance.Covariance

Genetic component Variance and covariance.

Std.Error

Standard error for combining ability effects.

C.D.

Critical Difference at 5 pecent for combining ability effects.

Add.Dom.Var

Additive and Dominance component of Variance.

Contribution.of.Line.Tester

Contribution of Lines, Testers and Line x Tester towards total variation.

Note

The block variable is inserted at the last if the experimental design is Alpha Lattice. For RCBD no need to have block factor.

Author(s)

Nandan Patil tryanother609@gmail.com

References

Kempthorne, O. (1957), Introduction to Genetic Statistics. John Wiley and Sons, New York. , 468-472. Singh, R. K. and Chaudhary, B. D. (1977). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi.

See Also

ltcchk

Examples

## Not run: #Line Tester analysis data with only crosses in RCBD
library(gpbStat)
data(rcbdltcmt)
result1 = ltcmt(rcbdltcmt, replication, line, tester, rcbdltcmt[,4:5])
result1

#Line Tester analysis data with only crosses in Alpha Lattice
library(gpbStat)
data(alphaltcmt)
result2 = ltcmt(alphaltcmt, replication, line, tester, alphaltcmt[,5:7], block)
result2

## End(Not run)

Analysis of Line x Tester data on single plant basis containing only Crosses laid out in RCBD or Alpha Lattice design.

Description

Analysis of Line x Tester data on single plant basis containing only Crosses laid out in RCBD or Alpha Lattice design.

Usage

ltcs(data, replication, line, tester, obs, y, block)

Arguments

data

dataframe containing following variables

replication

replication

line

line

tester

tester

obs

single plant observations

y

dependent variable

block

block (for alpha lattice design only)

Details

Analyzing the line by tester data single plant observations evaluated in RCBD and Alpha lattice design. All the factors are considered as fixed.

Value

Mean

Table of means.

ANOVA

ANOVA with all the factors.

GCA.Line

GCA effects of lines.

GCA.Tester

GCA effects of testers.

SCA

SCA effects of crosses.

CV

Coefficent of Variation.

Std.Error

Standard error for combining ability effects.

C.D.

Critical Difference at 5 pecent for combining ability effects.

Contribution.of.Line.Tester

Contribution of Lines, Testers and Line x Tester towards total variation.

Note

The block variable is inserted at the last if the experimental design is Alpha Lattice. For RCBD no need to have block factor.

Author(s)

Nandan L Patil tryanother609@gmail.com

References

Kempthorne, O. (1957), Introduction to Genetic Statistics. John Wiley and Sons, New York. , 468-472. Singh, R. K. and Chaudhary, B. D. (1977). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi. Arunachalam, V. (1974), The fallacy behind use of modified line x tester design. The Indian Journal of Genetics and Plant Breeding, 34: 280-287.

See Also

ltc, ltcmt

Examples

## Not run: #Line Tester analysis data with only crosses in RCBD
library(gpbStat)
data(rcbdltcs)
result1 = ltcs(rcbdltcs, replication, line, tester, obs, yield)
result1

#Line Tester analysis data with only crosses in Alpha Lattice
library(gpbStat)
data(alphaltcs)
result2 = ltcs(alphaltcs, replication, line, tester, obs, yield, block)
result2

## End(Not run)

Line x Tester data in RCBD

Description

The sample Line x Tester data containing only crosses laid out in Randomized Complete Block Design (RCBD).

Usage

data(rcbdltc)

Format

A data frame of four variables of 15 crosses derived from five lines and three testers.

replication

four replications

line

five inbred genotype

tester

three inbred genotype

yield

trait of intrest

See Also

alphaltcchk ,alphaltc ,rcbdltcchk

Examples

 result = ltc(rcbdltc, replication, line, tester, yield)

Line x Tester data (Crosses and Checks) in RCBD

Description

The sample Line x Tester data of containing crosses and checks laid out in Randomized Complete Block Design (RCBD). The data is composed of five lines, three testers and three checks.

Usage

data(rcbdltcchk)

Format

A dataframe of six variables.

replication

four replications

line

five lines

tester

three testers

yield

trait of intrest

See Also

rcbdltc ,alphaltc ,alphaltcchk

Examples

result = ltcchk(rcbdltcchk, replication, line, tester, check, yield)

Line x Tester data (only Crosses) in Randomized Complete Block design.

Description

The Line x Tester data of containing only crosses laid out in Randomized Complete Block design.

Usage

data(rcbdltcmt)

Format

A data frame of 15 crosses derived from five lines and three testers.

replication

four replications

line

five inbred genotype

tester

three inbred genotype

ph

plant height

eh

ear height

See Also

rcbdltc ,alphaltcchk ,rcbdltcchk ,alphaltcmt

Examples

 result = ltcmt(rcbdltcmt, replication, line, tester, rcbdltcmt[,4:5])

Line x Tester data (only Crosses) with single plant observations laid in RCBD design.

Description

The Line x Tester data containing single plant observations of only crosses laid out in RCBD design.

Usage

data(rcbdltcs)

Format

A data frame of 15 crosses derived from five lines and three testers.

replication

four replications

line

five inbred genotype

tester

three inbred genotype

obs

four single plant observations

yield

yield as a dependent trait

See Also

rcbdltcs ,alphaltcchk ,rcbdltcchk ,rcbdltcmt

Examples

 result = ltcs(rcbdltcs, replication, line, tester, obs, yield)