View source: R/mahala_design.R
mahala_design | R Documentation |
Compute the Mahalanobis distance using data from an experiment conducted in a randomized complete block design or completely randomized design.
mahala_design(
.data,
gen,
rep,
resp,
design = "RCBD",
by = NULL,
return = "distance"
)
.data |
The dataset containing the columns related to Genotypes,
replication/block and response variables, possible with grouped data passed
from |
gen |
The name of the column that contains the levels of the genotypes. |
rep |
The name of the column that contains the levels of the replications/blocks. |
resp |
The response variables. For example |
design |
The experimental design. Must be RCBD or CRD. |
by |
One variable (factor) to compute the function by. It is a shortcut
to |
return |
What the function return? Default is 'distance', i.e., the
Mahalanobis distance. Alternatively, it is possible to return the matrix of
means |
A symmetric matrix with the Mahalanobis' distance. If .data
is
a grouped data passed from dplyr::group_by()
then the results
will be returned into a list-column of data frames.
Tiago Olivoto tiagoolivoto@gmail.com
library(metan)
maha <- mahala_design(data_g,
gen = GEN,
rep = REP,
resp = everything(),
return = "covmat")
# Compute one distance for each environment (all numeric variables)
maha_group <- mahala_design(data_ge,
gen = GEN,
rep = REP,
resp = everything(),
by = ENV)
# Return the variance-covariance matrix of residuals
cov_mat <- mahala_design(data_ge,
gen = GEN,
rep = REP,
resp = c(GY, HM),
return = 'covmat')
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