This function implements the weighting method between mean performance and stability (Olivoto et al., 2019) considering different parametric and non-parametric stability indexes.
Usage
mps(
.data,
env,
gen,
rep,
resp,
block = NULL,
by = NULL,
random = "gen",
performance = c("blupg", "blueg"),
stability = "waasb",
ideotype_mper = NULL,
ideotype_stab = NULL,
wmper = NULL,
verbose = TRUE
)
Arguments
- .data
The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s).
- env
The name of the column that contains the levels of the environments.
- gen
The name of the column that contains the levels of the genotypes.
- rep
The name of the column that contains the levels of the replications/blocks.
- resp
The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example
resp = c(var1, var2, var3)
.- block
Defaults to
NULL
. In this case, a randomized complete block design is considered. If block is informed, then an alpha-lattice design is employed considering block as random to make use of inter-block information, whereas the complete replicate effect is always taken as fixed, as no inter-replicate information was to be recovered (Mohring et al., 2015).- by
One variable (factor) to compute the function by. It is a shortcut to
dplyr::group_by()
.This is especially useful, for example, when the researcher want to analyze environments within mega-environments. In this case, an object of class mps_grouped is returned.- random
The effects of the model assumed to be random. Defaults to
random = "gen"
. Seegamem_met()
to see the random effects assumed depending on the experimental design of the trials.- performance
Wich considers as mean performance. Either
blupg
(for Best Linear Unbiased Prediction) orblueg
(for Best Linear Unbiased Estimation)- stability
The stability method. One of the following:
"waasb"
The weighted average of absolute scores (Olivoto et al. 2019)."ecovalence"
The Wricke's ecovalence (Wricke, 1965)."Shukla"
The Shukla's stability variance parameter (Shukla, 1972)."hmgv"
The harmonic mean of genotypic values (Resende, 2007)."s2di"
The deviations from the Eberhart and Russell regression (Eberhart and Russell, 1966)."r2"
The determination coefficient of the Eberhart and Russell regression (Eberhart and Russell, 1966).."rmse"
The root mean squared error of the Eberhart and Russell regression (Eberhart and Russell, 1966)."wi"
Annicchiarico's genotypic confidence index (Annicchiarico, 1992)."polar"
Power Law Residuals as yield stability index (Doring et al., 2015)."acv"
Adjusted Coefficient of Variation (Doring and Reckling, 2018)"pi"
Lin e Binns' superiority index (Lin and Binns, 1988)."gai"
Geometric adaptability index (Mohammadi and Amri, 2008)."s1", "s2", "s3", and "s6"
Huehn's stability statistics (Huehn, 1979)."n1", "n2", "n3", and "n4"
Thennarasu's stability statistics (Thennarasu, 1995)."asv", "ev", "za", and "waas"
AMMI-based stability indexes (seeammi_indexes()
).
- ideotype_mper, ideotype_stab
The new maximum value after rescaling the response variable/stability index. By default, all variables in
resp
are rescaled so that de maximum value is 100 and the minimum value is 0 (i.e.,ideotype_mper = NULL
andideotype_stab = NULL
). It must be a character vector of the same length ofresp
if rescaling is assumed to be different across variables, e.g., if for the first variable smaller values are better and for the second one, higher values are better, thenideotype_mper = c("l, h")
must be used. For stability index in which lower values are better, useideotype_stab = "l"
. Character value of length 1 will be recycled with a warning message.- wmper
The weight for the mean performance. By default, all variables in
resp
have equal weights for mean performance and stability (i.e.,wmper = 50
). It must be a numeric vector of the same length ofresp
to assign different weights across variables, e.g., if for the first variable equal weights for mean performance and stability are assumed and for the second one, a higher weight for mean performance (e.g. 65) is assumed, thenwmper = c(50, 65)
must be used. Numeric value of length 1 will be recycled with a warning message.- verbose
Logical argument. If
verbose = FALSE
the code will run silently.
Value
An object of class mps
with the following items.
observed
: The observed value on a genotype-mean basis.performance
: The performance for genotypes (BLUPs or BLUEs)performance_res
: The rescaled values of genotype's performance, consideringideotype_mper
.stability
: The stability for genotypes, chosen with argumentstability
.stability_res
: The rescaled values of genotype's stability, consideringideotype_stab
.mps_ind
: The mean performance and stability for the traits.h2
: The broad-sense heritability for the traits.perf_method
: The method for measuring genotype's performance.wmper
: The weight for the mean performance.sense_mper
: The goal for genotype's performance (l
= lower,h
= higher).stab_method
: The method for measuring genotype's stability.wstab
: The weight for the mean stability.sense_stab
: The goal for genotype's stability (l
= lower,h
= higher).
References
Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. J. Genet. Breed. 46:269-278.
Doring, T.F., S. Knapp, and J.E. Cohen. 2015. Taylor's power law and the stability of crop yields. F. Crop. Res. 183: 294-302. doi:10.1016/j.fcr.2015.08.005
Doring, T.F., and M. Reckling. 2018. Detecting global trends of cereal yield stability by adjusting the coefficient of variation. Eur. J. Agron. 99: 30-36. doi:10.1016/j.eja.2018.06.007
Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing Varieties. Crop Sci. 6:36-40. doi:10.2135/cropsci1966.0011183X000600010011x
Huehn, V.M. 1979. Beitrage zur erfassung der phanotypischen stabilitat. EDV Med. Biol. 10:112.
Lin, C.S., and M.R. Binns. 1988. A superiority measure of cultivar performance for cultivar x location data. Can. J. Plant Sci. 68:193-198. doi:10.4141/cjps88-018
Mohammadi, R., & Amri, A. (2008). Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159(3), 419-432. doi:10.1007/s10681-007-9600-6
Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. doi:10.2134/agronj2019.03.0220
Resende MDV (2007) Matematica e estatistica na analise de experimentos e no melhoramento genetico. Embrapa Florestas, Colombo
Shukla, G.K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity. 29:238-245. doi:10.1038/hdy.1972.87
Thennarasu, K. 1995. On certain nonparametric procedures for studying genotype x environment interactions and yield stability. Ph.D. thesis. P.J. School, IARI, New Delhi, India.
Wricke, G. 1965. Zur berechnung der okovalenz bei sommerweizen und hafer. Z. Pflanzenzuchtg 52:127-138.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
# The same approach as mtsi()
# mean performance and stability for GY and HM
# mean performance: The genotype's BLUP
# stability: the WAASB index (lower is better)
# weights: equal for mean performance and stability
model <-
mps(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = everything())
#> Evaluating trait GY |====================== | 50% 00:00:01
Evaluating trait HM |============================================| 100% 00:00:03
#> Method: REML/BLUP
#> Random effects: GEN, GEN:ENV
#> Fixed effects: ENV, REP(ENV)
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> P-values for Likelihood Ratio Test of the analyzed traits
#> ---------------------------------------------------------------------------
#> model GY HM
#> COMPLETE NA NA
#> GEN 1.11e-05 5.07e-03
#> GEN:ENV 2.15e-11 2.27e-15
#> ---------------------------------------------------------------------------
#> All variables with significant (p < 0.05) genotype-vs-environment interaction
#> Mean performance: blupg
#> Stability: waasb
# The mean performance and stability after rescaling
model$mps_ind
#> # A tibble: 10 × 3
#> GEN GY HM
#> <chr> <dbl> <dbl>
#> 1 G1 57.6 56.2
#> 2 G10 0 35.0
#> 3 G2 59.9 17.8
#> 4 G3 95.5 67.9
#> 5 G4 45.7 58.6
#> 6 G5 40.0 61.1
#> 7 G6 45.9 85.5
#> 8 G7 45.2 51.3
#> 9 G8 77.3 90.6
#> 10 G9 16.3 58.7
# }