Performs a joint analysis of variance to check for the presence of genotype-vs-environment interactions using both randomized complete block and alpha-lattice designs.
anova_joint(.data, env, gen, rep, resp, block = NULL, verbose = TRUE)
.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. The analysis of variance is computed for each level of this factor. |
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 |
verbose | Logical argument. If |
A list where each element is the result for one variable containing the following objects:
anova: The two-way ANOVA table
model: The model of class lm
.
augment: Information about each observation in the dataset. This
includes predicted values in the fitted
column, residuals in the
resid
column, standardized residuals in the stdres
column,
the diagonal of the 'hat' matrix in the hat
, and standard errors for
the fitted values in the se.fit
column.
details: A tibble with the following data: Ngen
, the
number of genotypes; OVmean
, the grand mean; Min
, the minimum
observed (returning the genotype and replication/block); Max
the
maximum observed, MinGEN
the loser winner genotype, MaxGEN
,
the winner genotype.
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) # traditional usage approach j_an <- anova_joint(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything())#> variable GY #> --------------------------------------------------------------------------- #> Joint ANOVA table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) #> ENV 13.00 279.57 21.5057 62.33 0.00e+00 #> REP(ENV) 28.00 9.66 0.3451 3.57 3.59e-08 #> GEN 9.00 13.00 1.4439 14.93 2.19e-19 #> GEN:ENV 117.00 31.22 0.2668 2.76 1.01e-11 #> Residuals 252.00 24.37 0.0967 NA NA #> CV(%) 11.63 NA NA NA NA #> MSR+/MSR- 6.71 NA NA NA NA #> OVmean 2.67 NA NA NA NA #> --------------------------------------------------------------------------- #> #> variable HM #> --------------------------------------------------------------------------- #> Joint ANOVA table #> --------------------------------------------------------------------------- #> Source Df Sum Sq Mean Sq F value Pr(>F) #> ENV 13.00 5710 439.26 57.22 1.11e-16 #> REP(ENV) 28.00 215 7.68 2.70 2.20e-05 #> GEN 9.00 270 29.98 10.56 7.41e-14 #> GEN:ENV 117.00 1101 9.41 3.31 1.06e-15 #> Residuals 252.00 716 2.84 NA NA #> CV(%) 3.50 NA NA NA NA #> MSR+/MSR- 5.24 NA NA NA NA #> OVmean 48.09 NA NA NA NA #> --------------------------------------------------------------------------- #> #> All variables with significant (p < 0.05) genotype-vs-environment interaction #> Done!#>#>#> # A tibble: 420 x 6 #> ENV GEN REP factors GY HM #> <fct> <fct> <fct> <chr> <dbl> <dbl> #> 1 E1 G1 1 G1_1 2.42 46.5 #> 2 E1 G1 2 G1_2 2.40 46.0 #> 3 E1 G1 3 G1_3 2.27 47.1 #> 4 E1 G2 1 G2_1 2.96 45.4 #> 5 E1 G2 2 G2_2 2.94 44.8 #> 6 E1 G2 3 G2_3 2.81 45.9 #> 7 E1 G3 1 G3_1 2.95 45.9 #> 8 E1 G3 2 G3_2 2.92 45.3 #> 9 E1 G3 3 G3_3 2.80 46.4 #> 10 E1 G4 1 G4_1 2.65 48.3 #> # ... with 410 more rows#>#>#> # A tibble: 10 x 3 #> Parameters GY HM #> <chr> <chr> <chr> #> 1 Mean "2.67" "48.09" #> 2 SE "0.05" "0.21" #> 3 SD "0.92" "4.37" #> 4 CV "34.56" "9.09" #> 5 Min "0.67 (G10 in E11)" "38 (G2 in E14)" #> 6 Max "5.09 (G8 in E5)" "58 (G8 in E11)" #> 7 MinENV "E11 (1.37)" "E14 (41.03)" #> 8 MaxENV "E3 (4.06)" "E11 (54.2)" #> 9 MinGEN "G10 (2.47) " "G2 (46.66) " #> 10 MaxGEN "G8 (3) " "G5 (49.3) "# }