| Type: | Package |
| Title: | Genetic Variability Analysis for Plant Breeding Research |
| Version: | 0.1.0 |
| Description: | Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079). |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2020-09-25 16:16:38 UTC; Asus |
| Author: | Raj Popat [aut, cre], Rumit Patel [aut], Dinesh Parmar [aut] |
| Maintainer: | Raj Popat <popatrajc@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-09-29 08:30:02 UTC |
Analysis of Covariance
Description
Analysis of Covariance
Usage
ancova(data, genotypes, replication)
Arguments
data |
traits to be analyzed |
genotypes |
vector containing genotypes |
replication |
vector containing replications |
Value
ANCOVA, genotypic and phenotypic covariance
Examples
data(vardata)
ancova(vardata[3:11],vardata$Genotypes,vardata$Replication)
Estimation of Genetic Parameters
Description
Estimation of Genetic Parameters
Usage
gen.var(data, genotypevector, replicationvector)
Arguments
data |
traits to be analyzed |
genotypevector |
vector containing genotypes |
replicationvector |
vector containig replications |
Value
ANOVA, genotypic and phenotypic coefficient of variance, heritability, genetic advance and genetic advance as percentage of mean.
Examples
data(vardata)
gen.var(vardata[3:11],vardata$Genotypes,vardata$Replication)
Genotypic Correlation Analysis
Description
Genotypic Correlation Analysis
Usage
geno.corr(data, genotypes, replication)
Arguments
data |
traits to be analyzed |
genotypes |
vector containing genotypes |
replication |
vector containing replications |
Value
Genotypic correlation matrix
Examples
data(vardata)
geno.corr(vardata[3:11],vardata$Genotypes,vardata$Replication)
Genotypic Path Analysis
Description
Genotypic Path Analysis
Usage
geno.path(dependent.var, independent.var, genotypes, replication)
Arguments
dependent.var |
trait to be used a dependent variable |
independent.var |
traits to be used as an indpendent variables |
genotypes |
vector containing genotpes |
replication |
vector containing replications |
Value
Direct effects, indirect effects and residual
Examples
data(vardata)
# Grain yield is considered as a dependent variable
geno.path(vardata[11],vardata[3:10],vardata$Genotypes,vardata$Replication)
Phenotypic Correlation Analysis
Description
Phenotypic Correlation Analysis
Usage
pheno.corr(data, genotypes, replication)
Arguments
data |
traits to be analyzed |
genotypes |
vector containing genotypes |
replication |
vector containing replications |
Value
Phenotypic correlation
Examples
data(vardata)
pheno.corr(vardata[3:11],vardata$Genotypes,vardata$Replication)
Phenotypic Path Analysis
Description
Phenotypic Path Analysis
Usage
pheno.path(dependent.var, independent.var, genotypes, replication)
Arguments
dependent.var |
trait to be considered as a dependent variable |
independent.var |
traits to be connsidered as an independent variables |
genotypes |
vector containing genotypes |
replication |
vector containing replicatons |
Value
Direct effects, indirect effects and residual
Examples
data(vardata)
pheno.path(vardata[11],vardata[3:10],vardata$Genotypes,vardata$Replication)
Variability Data
Description
The data consists of genotypes, replications and nine traits
Usage
vardata
Format
The data has 11 columns and 120 rows
- Genotypes
40 genotypes
- Replication
3 replications
- DFF
Days to 50 per cent flowering
- PH
Plant height
- PL
Panicle length
- PW
Panicle weight
- HI
Harvest index
- TW
Test weight
- MILL
Milling percentage
- HRR
Head rice recovery
- GY
Grain Yield