Performs a within-environment analysis of variance in randomized complete block or alpha-lattice designs and returns values such as Mean Squares, p-values, coefficient of variation, heritability, and accuracy of selection.
anova_ind(.data, env, gen, rep, resp, block = NULL)
.data | The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s). |
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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 |
A list where each element is the result for one variable containing:
individual: A tidy tbl_df with the results of the individual analysis of variance with the following column names:
For analysis in alpha-lattice designs: ENV: The environment code; MEAN: The grand mean; MSG, MSCR, MSIB_R: The mean squares for genotype, replicates and incomplete blocks within replicates, respectively. FCG, FCR, FCIB_R: The F-calculated for genotype, replicates and incomplete blocks within replicates, respectively.PFG, PFCR, PFIB_R: The P-values for genotype, replicates and incomplete blocks within replicates, respectively. MSE: The mean square error. CV: coefficient of variation. h2: broad-sense heritability. AS: accuracy of selection (square root of h2)
For analysis in randomized complete block design: MSG, MSB: The mean squares for genotype and blocks, respectively. FCG, FCB: The F-calculated for genotype and blocks, respectively. PFG, PFB: The P-values for genotype and blocks, respectively. MSE: The mean square error. CV: coefficient of variation. h2: broad-sense heritability. AS: accuracy of selection (square root of h2)
MSRatio The ratio between the higher and lower residual mean square.
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) # ANOVA for all variables in data ind_an <- anova_ind(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything()) # mean for each environment get_model_data(ind_an)#>#>#> # A tibble: 14 x 3 #> ENV GY HM #> <chr> <dbl> <dbl> #> 1 E1 2.52 47.4 #> 2 E10 2.18 44.3 #> 3 E11 1.37 54.2 #> 4 E12 1.61 49.6 #> 5 E13 2.91 46.6 #> 6 E14 1.78 41.0 #> 7 E2 3.18 44.1 #> 8 E3 4.06 52.9 #> 9 E4 3.68 50 #> 10 E5 3.91 52.2 #> 11 E6 2.66 45.9 #> 12 E7 1.99 48.5 #> 13 E8 2.54 45.2 #> 14 E9 3.06 51.3#>#>#> # A tibble: 14 x 3 #> ENV GY HM #> <chr> <dbl> <dbl> #> 1 E1 0.0594 0.0293 #> 2 E10 0.0000110 0.00000302 #> 3 E11 0.244 0.107 #> 4 E12 0.000647 0.108 #> 5 E13 0.000210 0.0000180 #> 6 E14 0.153 0.00393 #> 7 E2 0.376 0.00402 #> 8 E3 0.123 0.0269 #> 9 E4 0.00712 0.000451 #> 10 E5 0.000110 0.126 #> 11 E6 0.0635 0.000163 #> 12 E7 0.00873 0.438 #> 13 E8 0.000131 0.00127 #> 14 E9 0.000562 0.00541# }