One-way analysis of variance of genotypes conducted in both randomized complete block and alpha-lattice designs.
gafem(
.data,
gen,
rep,
resp,
block = NULL,
by = NULL,
prob = 0.05,
verbose = TRUE
)
A list where each element is the result for one variable containing the following objects:
anova: The one-way ANOVA table.
model: The model with of lm
.
augment: Information about each observation in the dataset. This
includes predicted values in the fitted
column, residuals in the
resid
column, standardized residuals in the stdres
column,
the diagonal of the 'hat' matrix in the hat
, and standard errors for
the fitted values in the se.fit
column.
hsd: The Tukey's 'Honest Significant Difference' for genotype effect.
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 loser winner genotype, MaxGEN
,
the winner genotype.
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 resp = c(var1, var2, var3)
. Select helpers are also allowed.
Defaults to NULL
. In this case, a randomized complete
block design is considered. If block is informed, then a resolvable
alpha-lattice design (Patterson and Williams, 1976) is employed.
All effects, except the error, are assumed to be fixed. Use the
function gamem()
to analyze a one-way trial with mixed-effect
models.
One variable (factor) to compute the function by. It is a shortcut
to dplyr::group_by()
.This is especially useful, for example,
when the researcher want to fit a fixed-effect model for each environment.
In this case, an object of class gafem_grouped is returned.
mgidi()
can then be used to compute the mgidi index within each
environment.
The error probability. Defaults to 0.05.
Logical argument. If verbose = FALSE
the code are run
silently.
Tiago Olivoto tiagoolivoto@gmail.com
gafem
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; r_j is the effect of the jth replicate; 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. All effects, except the random error are assumed to be fixed.
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
get_model_data()
gamem()
# \donttest{
library(metan)
# RCBD
rcbd <- gafem(data_g,
gen = GEN,
rep = REP,
resp = c(PH, ED, EL, CL, CW))
# Fitted values
get_model_data(rcbd)
# ALPHA-LATTICE DESIGN
alpha <- gafem(data_alpha,
gen = GEN,
rep = REP,
block = BLOCK,
resp = YIELD)
# Fitted values
get_model_data(alpha)
# }
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