Description Usage Arguments Value Author(s) References See Also Examples
This function implements the weighting method between mean performance and stability (Olivoto et al., 2019) considering different parametric and nonparametric stability indexes.
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.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 
block 
Defaults to 
by 
One variable (factor) to compute the function by. It is a shortcut
to 
random 
The effects of the model assumed to be random. Defaults to

performance 
Wich considers as mean performance. Either 
stability 
The stability method. One of the following:

ideotype_mper, ideotype_stab 
The new maximum value after rescaling the
response variable/stability index. By default, all variables in 
wmper 
The weight for the mean performance. By default, all variables
in 
verbose 
Logical argument. If 
An object of class mps
with the following items.
observed
: The observed value on a genotypemean 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 broadsense 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).
Tiago Olivoto tiagoolivoto@gmail.com
Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in Northern Italy. J. Genet. Breed. 46:269278.
Doring, T.F., S. Knapp, and J.E. Cohen. 2015. Taylor's power law and the stability of crop yields. F. Crop. Res. 183: 294302. 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: 3036. doi: 10.1016/j.eja.2018.06.007
Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing Varieties. Crop Sci. 6:3640. 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. 68:193198. doi: 10.4141/cjps88018
Mohammadi, R., & Amri, A. (2008). Comparison of parametric and nonparametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159(3), 419432. doi: 10.1007/s1068100796006
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 multienvironment 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 genotypeenvironmental components of variability. Heredity. 29:238245. 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 52:127138.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  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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