data_simula | R Documentation |
g_simula()
simulate replicated genotype data.
ge_simula()
simulate replicated genotype-environment data.
ge_simula( ngen, nenv, nrep, nvars = 1, gen_eff = 20, env_eff = 15, rep_eff = 5, ge_eff = 10, res_eff = 5, intercept = 100, seed = NULL ) g_simula( ngen, nrep, nvars = 1, gen_eff = 20, rep_eff = 5, res_eff = 5, intercept = 100, seed = NULL )
ngen |
The number of genotypes. |
nenv |
The number of environments. |
nrep |
The number of replications. |
nvars |
The number of traits. |
gen_eff |
The genotype effect. |
env_eff |
The environment effect |
rep_eff |
The replication effect |
ge_eff |
The genotype-environment interaction effect. |
res_eff |
The residual effect. The effect is sampled from a normal
distribution with zero mean and standard deviation equal to |
intercept |
The intercept. |
seed |
The seed. |
The functions simulate genotype or genotype-environment data given a
desired number of genotypes, environments and effects. All effects are
sampled from an uniform distribution. For example, given 10 genotypes, and
gen_eff = 30
, the genotype effects will be sampled as runif(10, min = -30, max = 30)
. Use the argument seed
to ensure reproducibility. If more
than one trait is used (nvars > 1
), the effects and seed can be passed as
a numeric vector. Single numeric values will be recycled with a warning
when more than one trait is used.
A data frame with the simulated traits
Tiago Olivoto tiagoolivoto@gmail.com
library(metan) # Genotype data (5 genotypes and 3 replicates) gen_data <- g_simula(ngen = 5, nrep = 3, seed = 1) gen_data inspect(gen_data, plot = TRUE) aov(V1 ~ GEN + REP, data = gen_data) %>% anova() # Genotype-environment data # 5 genotypes, 3 environments, 4 replicates and 2 traits df <- ge_simula(ngen = 5, nenv = 3, nrep = 4, nvars = 2, seed = 1) ge_plot(df, ENV, GEN, V1) aov(V1 ~ ENV*GEN + ENV/REP, data = df) %>% anova() # Change genotype effect (trait 1 with fewer differences among genotypes) # Define different intercepts for the two traits df2 <- ge_simula(ngen = 10, nenv = 3, nrep = 4, nvars = 2, gen_eff = c(1, 50), intercept = c(80, 1500), seed = 1) ge_plot(df2, ENV, GEN, V2)
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