Compute variance-covariance and correlation matrices using data from a designed (RCBD or CRD) experiment.
Arguments
- .data
The data to be analyzed. It can be a data frame, possible with grouped data passed from
dplyr::group_by()
.- 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
resp = c(var1, var2, var3)
.- design
The experimental design. Must be RCBD or CRD.
- by
One variable (factor) to compute the function by. It is a shortcut to
dplyr::group_by()
. To compute the statistics by more than one grouping variable use that function.- type
What the matrices should return? Set to
NULL
, i.e., a list of matrices is returned. The argument type allow the following values'pcor', 'gcor', 'rcor'
, (which will return the phenotypic, genotypic and residual correlation matrices, respectively) or'pcov', 'gcov', 'rcov'
(which will return the phenotypic, genotypic and residual variance-covariance matrices, respectively). Alternatively, it is possible to get a matrix with the means of each genotype in each trait, by usingtype = 'means'
.
Value
An object of class covcor_design
containing the following
items:
geno_cov The genotypic covariance.
phen_cov The phenotypic covariance.
resi_cov The residual covariance.
geno_cor The phenotypic correlation.
phen_cor The phenotypic correlation.
resi_cor The residual correlation.
If .data
is a grouped data passed from dplyr::group_by()
then the results will be returned into a list-column of data frames.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
# List of matrices
data <- subset(data_ge2, ENV == 'A1')
matrices <- covcor_design(data,
gen = GEN,
rep = REP,
resp = c(PH, EH, NKE, TKW))
# Genetic correlations
gcor <- covcor_design(data,
gen = GEN,
rep = REP,
resp = c(PH, EH, NKE, TKW),
type = 'gcor')
# Residual (co)variance matrix for each environment
rcov <- covcor_design(data_ge2,
gen = GEN,
rep = REP,
resp = c(PH, EH, CD, CL),
by = ENV,
type = "rcov")
#> Warning: There were 4 warnings in `mutate()`.
#> The first warning was:
#> ℹ In argument: `data = purrr::map(.data$data, .fun, ...)`.
#> Caused by warning in `sqrt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
# }