This function implements the weighting method between mean performance and stability (Olivoto et al., 2019) considering different parametric and non-parametric stability indexes.
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
)
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,
considering ideotype_mper
.
stability
: The stability for genotypes, chosen with argument stability
.
stability_res
: The rescaled values of genotype's stability, considering
ideotype_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).
The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s).
The name of the column that contains the levels of the environments.
The name of the column that contains the levels of the genotypes.
The name of the column that contains the levels of the replications/blocks.
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)
.
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).
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.
The effects of the model assumed to be random. Defaults to
random = "gen"
. See gamem_met()
to see the random effects assumed
depending on the experimental design of the trials.
Wich considers as mean performance. Either blupg
(for
Best Linear Unbiased Prediction) or blueg
(for Best Linear Unbiased
Estimation)
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 (see
ammi_indexes()
).
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
and ideotype_stab = NULL
). It must be a character
vector of the same length of resp
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, then ideotype_mper = c("l, h")
must be used. For stability index in which lower values are
better, use ideotype_stab = "l"
. Character value of length 1 will be
recycled with a warning message.
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 of resp
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, then
wmper = c(50, 65)
must be used. Numeric value of length 1 will be
recycled with a warning message.
Logical argument. If verbose = FALSE
the code will run
silently.
Tiago Olivoto tiagoolivoto@gmail.com
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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1007/s10681-007-9600-6")
Olivoto, T., A.D.C. Lúcio, 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. tools:::Rd_expr_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. tools:::Rd_expr_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.
mtsi()
, mtmps()
, mgidi()
# \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())
# The mean performance and stability after rescaling
model$mps_ind
# }
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