stackTraits: Stack several trait columns into the long format used by...

In sommer: Solving Mixed Model Equations in R

Stack several trait columns into the long format used by multi-trait models

Description

Converts a wide data frame (one column per trait) into the long format used by multi-trait mmes models: one row per record and trait, a factor identifying the trait, a single response column and a record key that pairs the traits measured on the same experimental unit.

Usage

stackTraits(data, traits, keep = NULL, trait = "trait", value = "value",
            record = "record")

Arguments

data	A data frame in wide format.
traits	Character vector with the names of the trait columns to stack. Their order defines the levels of the trait factor.
keep	Columns of data copied to every stacked row. By default, all columns that are not in traits.
trait	Name of the new trait factor column.
value	Name of the new response column.
record	Name of the new record-key factor column.

Details

Rows are stacked trait by trait, so row i of trait t is row (t-1)n+i of the output. The record key is taken from the row names of data when these are unique and not the default 1..n; otherwise it is "r1", "r2", .... Missing trait values are kept as NA (they are handled by the naMethodY rules of mmes, so incomplete records are allowed).

Using the record key in the residual, rcov = ~ vsm(usm(trait), ism(record)), pairs the residuals of the traits of each unit explicitly. With ism(units) the pairing is implied by the order of the rows within each trait instead.

Value

A data frame with the keep columns and the record, trait and value columns, with length(traits) * nrow(data) rows.

See Also

mmes, covmatrix_mmes, familym

Examples

set.seed(17)
n <- 60
DT <- data.frame(id = factor(rep(seq_len(n), each = 3)))
u <- matrix(rnorm(n * 2), n) 
DT$trait_a <- u[DT$id, 1] + rnorm(nrow(DT), sd = 0.7)
DT$trait_b <- u[DT$id, 2] + rnorm(nrow(DT), sd = 1)
DTL <- stackTraits(DT, traits = c("trait_a", "trait_b"), keep = "id")
head(DTL)
table(DTL$trait)

# bivariate model with the residual paired through the record key
fit <- mmes(value ~ trait,
  random = ~ vsm(usm(trait), ism(id)),
  rcov = ~ vsm(usm(trait), ism(record)),
  data = DTL,
  control = mmes_control(diagnostics = list(verbose = FALSE)))
covmatrix_mmes(fit, 1)$correlation   # genetic correlation

sommer documentation built on Oct. 24, 2026, 1:08 a.m.