I am performing a multivariate redundancy analysis in vegan with forward selection. At one point i want to extract the significant terms in the reduced model to form the right hand side of a new model. I know where these terms are located in the output of the function, but i fail to extract them:
library(vegan)
data(dune)
data(dune.env)
mod0 <- rda(dune ~ 1, dune.env) # Model with intercept only
mod1 <- rda(dune ~ ., dune.env) # Model with all explanatory variables
obj <- ordistep(mod0, scope = formula(mod1))
The significant model variables are here:
obj$terminfo$terms ## or obj$terms
# dune ~ Management + Moisture **<------ this i need**
# attr(,"variables")
# list(dune, Management, Moisture)
# attr(,"factors")
# Management Moisture
# dune 0 0
# Management 1 0
# Moisture 0 1
# attr(,"term.labels")
# [1] "Management" "Moisture" **<------ alternatively this**
# attr(,"order")
# [1] 1 1
# attr(,"intercept")
# [1] 1
# attr(,"response")
# [1] 1
# attr(,".Environment")
# <environment: R_GlobalEnv
I tried different approaches with $,[[]], attr/which, and [], but failed. In the end, i would to create a vector:
rhs <- paste(model.terms, collapse = " + ").
How would one extract the terms located in the given object?
terms(obj) will extract terms, formula(obj) will extract the formula, and update() can be used update the ordination result object. For instance, formula can be changed with update().