gamem | R Documentation |
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 )
.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 |
block |
Defaults to |
by |
One variable (factor) to compute the function by. It is a shortcut
to |
prob |
The probability for estimating confidence interval for BLUP's prediction. |
verbose |
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:
\loadmathjax
\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.
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 \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
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
modellme The mixed-effect model of class lmerMod
.
residuals The residuals of the mixed-effect model.
model_lm The fixed-effect model of class lm
.
residuals_lm The residuals of the fixed-effect 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.
formula The formula used to fit the mixed-model.
Tiago Olivoto tiagoolivoto@gmail.com
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
get_model_data()
waasb()
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")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.