rpy2: Invalid Model formula in Extract Vars (tmp, simplify = TRUE)

Question

I am attempting to use the R "NADA" package using the rpy2 interface in Python. The end goal is to perform survival analysis on left-censored environmental data. Things seem to be interacting correctly between Python and R for other functions, and I am able to perform a test function in R, but I get an error when attempting the same through rpy2.

This is my code in Python. It is entirely fictitious data.

from rpy2.robjects import FloatVector, BoolVector, FactorVector
from rpy2.robjects.packages import importr

nada = importr('NADA')
obs = FloatVector([1.0,2.0,3.0,5.0,56.0,1.0,4.0])
nds = BoolVector([False, True, True, True, True, False, True])
groups = FactorVector([1,0,1,0,1,1,0])

nada.cendiff(obs, nds, groups)

This is the error message I receive:

Traceback (most recent call last):
  File "C:/Users/XXXXXXX/rpy2_test.py", line 9, in <module>
    nada.cendiff(obs, nds, groups)
  File "C:\Program Files\Python35\lib\site-packages\rpy2\robjects\functions.py", line 178, in __call__
    return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
  File "C:\Program Files\Python35\lib\site-packages\rpy2\robjects\functions.py", line 106, in __call__
    res = super(Function, self).__call__(*new_args, **new_kwargs)
rpy2.rinterface.RRuntimeError: Error in terms.formula(tmp, simplify = TRUE) : 
  invalid model formula in ExtractVars

This code works fine in the R terminal:

library("NADA")
cendiff(c(1.0,2.0,3.0,5.0,56.0,1.0,4.0), c(FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE), factor(c(1,0,1,0,1,1,0)))

I tried adding some print lines at the rpy2 error lines listed, and suspect there may be an issue with rpy2 removing the levels from the factor vector when sending them to the function. However, I'm in new territory and that may just be a red herring.

If anyone can lend some insight or offer an alternative, I would appreciate it. I have a lot of data processing coded in Python and going all R isn't a good option, but R has more analysis options so I was hoping rpy2 would do the trick.

Answer 1

When in doubt about whether rpy2 and/or one of its conversion rules are doing something unexpected, it is relatively easy to check it. For example here:

from rpy2.robjects.vectors import FactorVector
from rpy2.robjects import r, globalenv

# factor with rpy2
groups = FactorVector([1,0,1,0,1,1,0])
# bind it to symbol in R's GlobalEnv
globalenv['groups_rpy2'] = groups

# it is the same as building the factor in R ?
r("""
    ...: groups <- factor(c(1,0,1,0,1,1,0))
    ...: print(identical(groups, groups_rpy2))
    ...: """)
[1]
 TRUE

# apparently so

I am suspecting that this is caused by the fact that (unevaluated) expression statements are used in the R library you are using, and rpy2 is passing anonymous R objects. I had a quick glance at that code and I can see:

setMethod("cendiff", 
          signature(obs="numeric", censored="logical", groups="factor"), 
          cencen.vectors.groups)

and

cencen.vectors.groups =
function(obs, censored, groups, ...)
{
    cl = match.call()
    f = substitute(Cen(a, b)~g, list(a=cl[[2]], b=cl[[3]], g=cl[[4]]))
    f = as.formula(f)
    environment(f) = parent.frame()
    callGeneric(f, ...)
}

One way to work around that is to bind your objects to symbols in an R namespace/environment and evaluate the call in that namespace. It could be done with any R environment but if using "GlobalEnv" (in that case remember that the content of GlobalEnv persists until the embedded R is closed):

from rpy2.robjects.packages import importr
base = importr('base')
# bind to R symbols
globalenv["obs"] = obs
globalenv["nds"] = nds
globalenv["groups"] = groups

# make the call 
nada.cendiff(base.as_symbol('obs'),
             base.as_symbol('nds'),
             base.as_symbol('groups'))

(See an other use of as_symbol in Minimal example of rpy2 regression using pandas data frame)