Performs a stability analysis based on the Power Law Residuals (POLAR) statistics (Doring et al., 2015). POLAR is the residuals from the linear regression of \(log(\sigma^2\)) against \(log(\mu\)) and can be used as a measure of crop stability with lower stability (relative to all samples with that mean yield) indicated by more positive POLAR values, and higher stability (relative to all samples with that mean yield) indicated by more negative POLAR values.
Arguments
- .data
The dataset containing the columns related to Environments, Genotypes 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.
- resp
The response variable(s). To analyze multiple variables in a single procedure use, for example,
resp = c(var1, var2, var3)
.- base
The base with respect to which logarithms are computed. Defaults to
10
.- verbose
Logical argument. If
verbose = FALSE
the code will run silently.
Value
An object of class ge_acv
, which is a list containing the
results for each variable used in the argument resp
. For each
variable, a tibble with the following columns is returned.
GEN the genotype's code.
POLAR The Power Law Residuals
POLAR_R The rank for the ACV value.
References
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
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
out <- ge_polar(data_ge2, ENV, GEN, c(EH, PH, EL, CD, ED, NKE))
#> Evaluating trait EH |======= | 17% 00:00:00
Evaluating trait PH |=============== | 33% 00:00:00
Evaluating trait EL |====================== | 50% 00:00:00
Evaluating trait CD |============================= | 67% 00:00:00
Evaluating trait ED |===================================== | 83% 00:00:01
Evaluating trait NKE |===========================================| 100% 00:00:01
gmd(out)
#> Class of the model: ge_polar
#> Variable extracted: POLAR
#> # A tibble: 13 × 7
#> GEN EH PH EL CD ED NKE
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 H1 0.0950 -0.0439 -1.01 -1.36 -0.796 0.299
#> 2 H10 0.115 0.0603 0.0782 0.0570 0.334 -1.25
#> 3 H11 -0.0634 -0.0287 0.174 0.0756 -0.115 -0.699
#> 4 H12 0.00927 -0.220 -0.0406 0.0842 -0.424 0.0711
#> 5 H13 -0.292 -0.311 -0.218 0.00976 0.342 0.530
#> 6 H2 0.0316 0.0582 -0.480 -0.0412 0.407 0.394
#> 7 H3 0.194 0.246 0.393 0.336 -0.0112 0.367
#> 8 H4 0.167 0.139 -0.166 -0.120 0.0759 0.248
#> 9 H5 0.0240 0.00211 -0.0905 -0.643 -0.264 -0.991
#> 10 H6 -0.303 -0.0475 0.590 0.496 0.373 0.618
#> 11 H7 -0.147 -0.0672 0.255 0.318 -0.158 -0.124
#> 12 H8 0.0553 0.0605 0.297 0.425 0.133 0.239
#> 13 H9 0.114 0.153 0.218 0.367 0.103 0.298
# }