RRuntimeError: Error in qr.lm(object) : lm object does not have a proper 'qr' component. Rank zero or should not have used lm(.., qr=FALSE)

Question

I am using rpy2 to access a function which accepts the variables and then predicts the output for a GAM Model .

However, I am facing the following error during python runtime:

RRuntimeError                             Traceback (most recent call last)
<ipython-input-12-e571fb4e8082> in <module>
----> 1 gam_model(0.1, 13000, 1300, .5, 9000, 4, 10)

{PATH_TO_ENV}\lib\site-packages\rpy2\robjects\functions.py in __call__(self, *args, **kwargs)
    176                 v = kwargs.pop(k)
    177                 kwargs[r_k] = v
--> 178         return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
    179 
    180 pattern_link = re.compile(r'\\link\{(.+?)\}')

{PATH_TO_ENV}\lib\site-packages\rpy2\robjects\functions.py in __call__(self, *args, **kwargs)
    104         for k, v in kwargs.items():
    105             new_kwargs[k] = conversion.py2ri(v)
--> 106         res = super(Function, self).__call__(*new_args, **new_kwargs)
    107         res = conversion.ri2ro(res)
    108         return res

RRuntimeError: Error in qr.lm(object) : lm object does not have a proper 'qr' component.
 Rank zero or should not have used lm(.., qr=FALSE)

There does not seem to be a problem in in the R side as I tried the script alone and was able to run the function as expected. Following is the code for the R function and the Python call respectively. Also I tested the integration on a simple Linear regression model with one variable and it was working fine as well.

The R function:

gam_model <- function(var1, var2, 
                      var3, var4, var5, 
                      var6, var7, ...){
  x <- data.frame(var1=var1, var2=var2, 
                  var3=var3, var4=var4,  
                  var5=var5, var6=var6, var7=var7)
  model <- readRDS("models/gam_1.rds")
  
  result <- predict(model, x)
  
  return(result)
}

The python call:

from rpy2 import robjects
from rpy2.robjects import pandas2ri

r = robjects.r
r['source']('model_functions.R')

gam_model = robjects.globalenv['gam_model']
y_pred = gam_model(0.1, 13000, 1300, .5, 9000, 4, 10)

Answer 1

I have identified the issue as the package 'mgcv' is not installed. Installing will resolve the issue.