Package 'gpbStat'

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 [email protected]

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 [email protected]

References

Pour‐Aboughadareh, A., Yousefian, M., Moradkhani, H., Moghaddam Vahed, M., Poczai, P., & 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 [email protected]

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 [email protected]

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 [email protected]

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 [email protected]

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)

---