gafem | R Documentation |
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 )
.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 error probability. Defaults to 0.05. |
verbose |
Logical argument. If |
gafem
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; \mjseqnr_j is the effect of the jth
replicate; 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. All effects, except the random error are assumed to be fixed.
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.
Tiago Olivoto tiagoolivoto@gmail.com
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
get_model_data()
gamem()
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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