gafem {metan} | R Documentation |
Genotype analysis by fixed-effect models
Description
One-way analysis of variance of genotypes conducted in both randomized complete block and alpha-lattice designs.
Usage
gafem(
.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 |
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 |
Details
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:
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.
Value
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 theresid
column, standardized residuals in thestdres
column, the diagonal of the 'hat' matrix in thehat
, and standard errors for the fitted values in these.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.
Author(s)
Tiago Olivoto tiagoolivoto@gmail.com
References
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
See Also
Examples
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)