metan (version 1.10.0)

gamem: Genotype analysis by mixed-effect models

Description

Analysis of genotypes in single experiments using mixed-effect models with estimation of genetic parameters.

Usage

gamem(
  .data,
  gen,
  rep,
  resp,
  block = NULL,
  by = NULL,
  prob = 0.05,
  verbose = TRUE
)

Arguments

.data

The dataset containing the columns related to, Genotypes, replication/block and response variable(s).

gen

The name of the column that contains the levels of the genotypes, that will be treated as random effect.

rep

The name of the column that contains the levels of the replications (assumed to be fixed).

resp

The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example resp = c(var1, var2, var3). Select helpers are also allowed.

block

Defaults to NULL. In this case, a randomized complete block design is considered. If block is informed, then an alpha-lattice design is employed considering block as random to make use of inter-block information, whereas the complete replicate effect is always taken as fixed, as no inter-replicate information was to be recovered (Mohring et al., 2015).

by

One variable (factor) to compute the function by. It is a shortcut to group_by().This is especially useful, for example, when the researcher what to fit a mixed-effect model for each environment. In this case, an object of class gamem_grouped is returned. mgidi can then be used to compute the mgidi index within each environment.

prob

The probability for estimating confidence interval for BLUP's prediction.

verbose

Logical argument. If verbose = FALSE the code are run silently.

Value

An object of class gamem or gamem_grouped, which is a list with the following items for each element (variable):

• fixed: Test for fixed effects.

• random: Variance components for random effects.

• LRT: The Likelihood Ratio Test for the random effects.

• BLUPgen: The estimated BLUPS for genotypes

• ranef: The random effects of the model

• 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 winner genotype, MaxGEN, the loser genotype.

• ESTIMATES: A tibble with the values:

  • Gen_var, the genotypic variance and ;

  • rep:block_var block-within-replicate variance (if an alpha-lattice design is used by informing the block in block);

  • Res_var, the residual variance;

  • Gen (%), rep:block (%), and Res (%) the respective contribution of variance components to the phenotypic variance;

  • H2, broad-sense heritability;

  • h2mg, heritability on the entry-mean basis;

  • Accuracy, the accuracy of selection (square root of h2mg);

  • CVg, genotypic coefficient of variation;

  • CVr, residual coefficient of variation;

  • CV ratio, the ratio between genotypic and residual coefficient of variation.

• residuals: The residuals of the model.

• formula The formula used to fit the model.

Details

gamem analyses data from a one-way genotype testing experiment. By default, a randomized complete block design is used according to the following model:

Y_ij = m + g_i + r_j + e_ij where Y_ij is the response variable of the ith genotype in the jth block; m is the grand mean (fixed); g_i is the effect of the ith genotype (assumed to be random); r_j is the effect of the jth replicate (assumed to be fixed); and e_ij is the random error.

When block is informed, then a resolvable alpha design is implemented, according to the following model:

Y_ijk = m + g_i + r_j + b_jk + e_ijk where where y_ijk is the response variable of the ith genotype in the kth block of the jth replicate; m is the intercept, t_i is the effect for the ith genotype r_j is the effect of the jth replicate, b_jk is the effect of the kth incomplete block of the jth replicate, and e_ijk is the plot error effect corresponding to y_ijk.

References

Mohring, J., E. Williams, and H.-P. Piepho. 2015. Inter-block information: to recover or not to recover it? TAG. Theor. Appl. Genet. 128:1541-54. 10.1007/s00122-015-2530-0

See Also

get_model_data waasb

Examples

# NOT RUN {
library(metan)

# fitting the model considering an RCBD
# Genotype as random effects

rcbd <- gamem(data_g,
             gen = GEN,
             rep = REP,
             resp = c(PH, ED, EL, CL, CW, KW, NR, TKW, NKE))

# Likelihood ratio test for random effects
get_model_data(rcbd, "lrt")


# Variance components
get_model_data(rcbd, "vcomp")

# Genetic parameters
get_model_data(rcbd, "genpar")

# random effects
get_model_data(rcbd, "ranef")

# Predicted values
predict(rcbd)

# fitting the model considering an alpha-lattice design
# Genotype and block-within-replicate as random effects
# Note that block effect was now informed.

alpha <- gamem(data_alpha,
               gen = GEN,
               rep = REP,
               block = BLOCK,
               resp = YIELD)
# Genetic parameters
get_model_data(alpha, "genpar")

# Random effects
get_model_data(alpha, "ranef")
# }
# NOT RUN {
# }