protect against NaN when fitting fails

Author: grlloyd

R/forward_selection_by_rank_class.R

@@ -181,7 +181,16 @@ eval_loess=function(x,X,Y,k=10,p=0.66)
         yy2=Y[X %in% xx2]
 
 
-        loessMod <- loess(yy ~ xx, span=x) # 25% smoothing span
+        loessMod <- loess(yy ~ xx, span=x)
+
+        # check for NaN
+        if (any(is.nan(loessMod$fitted))){
+            residual[i]=99999
+        } else {
+
+            smoothed=stats::predict(loessMod,newdata=xx2)
+            residual[i]=sum((smoothed-yy2)^2)
+        }
         smoothed=stats::predict(loessMod,newdata=xx2)
         residual[i]=sum((smoothed-yy2)^2)
     }