For tasks such as modelling and plotting, an R formula can be a terse, yet readable, way of expressing what is wanted.
In R, it generally looks like:
x <- 1:10 y <- x + rnorm(10, sd=0.2) fit <- lm(y ~ x)
In the call to lm, the argument is a formula, and it can read like model y using x. A formula is a R language object, and the terms in the formula are evaluated in the environment it was defined in. Without further specification, that environment is the environment in which the the formula is created.
robjects.Formula is representing an R formula.
import array from rpy2.robjects import IntVector, Formula from rpy2.robjects.packages import importr stats = importr('stats') x = IntVector(range(1, 11)) y = x.ro + stats.rnorm(10, sd=0.2) fmla = Formula('y ~ x') env = fmla.environment env['x'] = x env['y'] = y fit = stats.lm(fmla)
One drawback with that approach is that pretty printing of the fit object is not quite as good as what one would expect when working in R: the call item now displays the code for the function used to perform the fit.
If one still wants to avoid polluting the R global environment, the answer is to evaluate R call within the environment where the function is defined.
from rpy2.robjects import Environment eval_env = Environment() eval_env['fmla'] = fmla base = importr('base') fit = base.eval.rcall(base.parse(text = 'lm(fmla)'), stats._env)
Other options are:
Evaluate R code on the fly so we that model fitting function has a symbol in R
fit = robjects.r('lm(%s)' %fmla.r_repr())
Evaluate R code where all symbols are defined
- class rpy2.robjects.Formula(formula, environment=rinterface.globalenv)[source]¶
- property environment¶
R environment in which the formula will look for its variables.