| Title: | Partially Replicated (p-Rep) Designs |
|---|---|
| Description: | Early generation breeding trials are to be conducted in multiple environments where it may not be possible to replicate all the lines in each environment due to scarcity of resources. For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properties of any block design. A construction procedure for p-Rep designs was given by Williams et al.(2011) <doi:10.1002/bimj.201000102> which was tedious and time consuming. Here, in this package, five different methods have been given to generate p-Rep designs easily. |
| Authors: | Vinaykumar L.N. [aut, cre], Cini Varghese [aut, ctb], Mohd Harun [aut, ctb], Ashutosh Dalal [aut, ctb], Sayantani Karmakar [aut, ctb], Vinayaka [aut, ctb] |
| Maintainer: | Vinaykumar L.N. <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.2.0 |
| Built: | 2024-11-01 11:41:31 UTC |
| Source: | CRAN |
This function generates incidence matrix, information matrix, canonical efficiency factor,variance factor between associates and average variance for the input design
NCEV(design)NCEV(design)
design |
p-rep design in matrix form considering rows as blocks |
This function generates incidence matrix, information matrix, canonical efficiency factor, variance factor between associates and average variance factor for the input design
Williams E, Piepho HP, Whitaker D (2011)<https://doi.org/10.1002/bimj.201000102>
library(pRepDesigns) design=matrix(1:9, nrow = 3, ncol = 3) NCEV(design)library(pRepDesigns) design=matrix(1:9, nrow = 3, ncol = 3) NCEV(design)
This function generates first series of p-rep designs for given values of v, m and s. The input should satisfy the condition v=2*m*s*(s-1), m>=1 and s>=3.
pRep1(v, m, s)pRep1(v, m, s)
v |
Total number of treatments or breeding lines or entries |
m |
positive integer (>=1) |
s |
positive integer (>=3) |
This function generates p-rep designs with parameters:
v = 2ms(s-1) : number of treatments,
b_1 = 2(s-1) : first set of blocks of size k_1 = ms,
b_2 = 2s : second set of blocks of size k_2 = 2m(s-1) and
r = 3 : number of replications.
e = 2 : number of environments,
This function also generates incidence matrix, information matrix, canonical efficiency factor and average variance factor of the generated p-rep design.
Williams E, Piepho HP, Whitaker D (2011)<doi:10.1002/bimj.201000102>
library(pRepDesigns) pRep1(24, 2, 3)library(pRepDesigns) pRep1(24, 2, 3)
For the specified values of v and p, this function generates the second series of p-rep designs. The input should meet the condition that v=10*p where, p>=2.
pRep2(v, p)pRep2(v, p)
v |
Total number of treatments or breeding lines or entries |
p |
positive integer (>=2) |
This function generates a new series of p-rep designs with parameters:
v = 10p number of treatments,
e = 2 number of environments,
b = 10 blocks of size k = 3p and
r = 3 number of replications.
This function also generates canonical efficiency factor and average variance factor of the generated p-rep design.
Williams E, Piepho HP, Whitaker D (2011)<https://doi.org/10.1002/bimj.201000102>
library(pRepDesigns) pRep2(20,2)library(pRepDesigns) pRep2(20,2)
For the specified values of v and p, this function generates a series of p-rep designs. The input should meet the condition that v=6*p where, p>=2.
pRep3(v, p)pRep3(v, p)
v |
Total number of treatments or breeding lines or entries |
p |
positive integer (>=2) |
This function generates a new series of p-rep designs with parameters:
v = 6p : number of treatments,
e = 2 : number of environments,
b = 6 : blocks of size k = 3p and
r = 3 : number of replications.
Canonical efficiency factor and average variance factor of the generated p-rep design.
Williams E, Piepho HP, Whitaker D (2011)<https://doi.org/10.1002/bimj.201000102>
library(pRepDesigns) pRep3(18, 2)library(pRepDesigns) pRep3(18, 2)
The first step of this function involves generating a higher associate PBIB (partially balanced incomplete block) design consisting of two sets of blocks. Subsequently, by creating various combinations of these two sets of blocks, it generates multiple p-rep designs in different environments with unequal block sizes.
pRep4(v, m, s, randomized_layout = FALSE)pRep4(v, m, s, randomized_layout = FALSE)
v |
Total number of treatments (v = 2ms*(s-1)) |
m |
Positive integer (m>=1) |
s |
Positive integer (s>=3) |
randomized_layout |
TRUE or FALSE. By default it is FALSE. |
This function calculates design parameters (v, b1, b2, r, k1, k2), average variance factors, and canonical efficiency factors of generated designs.
## Not run: library(pRepDesigns) pRep4(48, 2, 4) ## End(Not run)## Not run: library(pRepDesigns) pRep4(48, 2, 4) ## End(Not run)
The primary purpose of this function is to generate various proper p-rep designs for multi-environmental trials.
pRep5(v, m, s, randomized_layout = FALSE)pRep5(v, m, s, randomized_layout = FALSE)
v |
Total number of treatments (v = 2ms^2) |
m |
Positive integer (m>=1) |
s |
Positive integer (s>=2) |
randomized_layout |
TRUE or FALSE. By default it is FALSE. |
This function calculates design parameters (v, b, r, k), average variance factors, and canonical efficiency factors of generated designs.
## Not run: library(pRepDesigns) pRep5(64, 2, 4) ## End(Not run)## Not run: library(pRepDesigns) pRep5(64, 2, 4) ## End(Not run)