Analysis of genotypes in single experiments using mixed-effect models with estimation of genetic parameters.
gamem( .data, gen, rep, resp, block = NULL, by = NULL, prob = 0.05, verbose = TRUE )
The dataset containing the columns related to, Genotypes, replication/block and response variable(s).
The name of the column that contains the levels of the genotypes, that will be treated as random effect.
The name of the column that contains the levels of the replications (assumed to be fixed).
The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example
One variable (factor) to compute the function by. It is a shortcut
The probability for estimating confidence interval for BLUP's prediction.
Logical argument. If
gamem analyses data from a one-way genotype testing experiment.
By default, a randomized complete block design is used according to the following model:
\mjsdeqnY_ij = m + g_i + r_j + e_ij
where \mjseqnY_ij is the response variable of the ith genotype in the jth block;
m is the grand mean (fixed); \mjseqng_i is the effect of the ith genotype
(assumed to be random); \mjseqnr_j is the effect of the jth replicate (assumed to be fixed);
and \mjseqne_ij is the random error.
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 \mjseqny_ijk is the response variable of the ith genotype in the kth block of the jth replicate; m is the intercept, \mjseqnt_i is the effect for the ith genotype \mjseqnr_j is the effect of the jth replicate, \mjseqnb_jk is the effect of the kth incomplete block of the jth replicate, and \mjseqne_ijk is the plot error effect corresponding to \mjseqny_ijk.
An object of class
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
modellme The mixed-effect model of class
residuals The residuals of the mixed-effect model.
model_lm The fixed-effect model of class
residuals_lm The residuals of the fixed-effect model.
Details: A tibble with the following data:
number of genotypes;
OVmean, the grand mean;
Min, the minimum
observed (returning the genotype and replication/block);
MinGEN the winner 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
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
CVg, genotypic coefficient of variation;
CVr, residual coefficient of variation;
CV ratio, the ratio between genotypic and residual coefficient of
formula The formula used to fit the mixed-model.
Tiago Olivoto firstname.lastname@example.org
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. doi: 10.1007/s00122-015-2530-0
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")
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