R - predict command error "undefined columns selected"

Question

I’m a newbie to R, and I’m having trouble with an R predict command. I receive this error

 Error in `[.data.frame`(newdata, , as.character(object$formula[[2]])) : 
  undefined columns selected

when I execute this command:

model.predict <- predict.boosting(model,newdata=test)

Here is my model:

model <- boosting(Y~x1+x2+x3+x4+x5+x6+x7, data=train)

And here is the structure of my test data: str(test)

'data.frame':   343 obs. of  7 variables:
 $ x1: Factor w/ 4 levels "Americas","Asia_Pac",..: 4 2 4 2 4 3 3 3 4 1 ...
 $ x2: Factor w/ 5 levels "Fifth","First",..: 3 3 2 2 4 2 4 4 1 1 ...
 $ x3: Factor w/ 3 levels "Best","Better",..: 2 3 1 1 3 2 2 1 3 3 ...
 $ x4: Factor w/ 2 levels "Female","Male": 1 1 2 1 1 2 1 2 2 2 ...
 $ x5: int  82 55 47 31 6 53 77 68 76 86 ...
 $ x6: num  22.8 14.6 25.5 38.3 7.9 32.8 4.6 34.2 36.7 21.7 ...
 $ x7: num  0.679 0.925 0.897 0.684 0.195 ...

And the structure of my training data:

$ RecordID: int  1 2 3 4 5 6 7 8 9 10 ...
 $ x1      : Factor w/ 4 levels "Americas","Asia_Pac",..: 1 2 2 3 1 1 1 2 2 4 ...
 $ x2      : Factor w/ 5 levels "Fifth","First",..: 5 5 3 2 5 5 5 4 3 2 ...
 $ x3      : Factor w/ 3 levels "Best","Better",..: 2 3 2 2 3 1 2 3 1 1 ...
 $ x4      : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 1 2 2 1 1 ...
 $ x5      : int  1 67 75 51 84 33 21 80 48 5 ...
 $ x6      : num  21 13.8 30.3 11.9 1.7 13.2 33.9 17 3.4 19.5 ...
 $ x7      : num  0.35 0.85 0.73 0.39 0.47 0.13 0.2 0.12 0.64 0.11 ...
 $ Y       : Factor w/ 2 levels "Green","Yellow": 2 2 1 2 2 2 1 2 2 2 ..

I think there’s a problem with the structure of the test data, but I can’t find it, or I have a mis-understanding as to the structure of the “predict” command. Note that if I run the predict command on the training data, it works. Any suggestions as to where to look?

Thanks!

Answer 1

predict.boosting() expects to be given the actual labels for the test data, so it can calculate how well it did (as in the confusion matrix shown below).

library(adabag) 

data(iris)

iris.adaboost <- boosting(Species~Sepal.Length+Sepal.Width+Petal.Length+
      Petal.Width, data=iris, boos=TRUE, mfinal=10)

# make a 'test' dataframe without the classes, as in the question
iris2 <- iris
iris2$Species <- NULL

# replicates the error
irispred=predict.boosting(iris.adaboost, newdata=iris2)
#Error in `[.data.frame`(newdata, , as.character(object$formula[[2]])) : 
#  undefined columns selected

Here's working example, drawn largely from the help file just so there is a working example here (and to demonstrate the confusion matrix).

# first create subsets of iris data for training and testing  
sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
iris3 <- iris[sub,]
iris4 <- iris[-sub,]

iris.adaboost <- boosting(Species ~ ., data=iris3, mfinal=10)

# works
iris.predboosting<- predict.boosting(iris.adaboost, newdata=iris4)

iris.predboosting$confusion
#               Observed Class
#Predicted Class setosa versicolor virginica
#     setosa         50          0         0
#     versicolor      0         50         0
#     virginica       0          0        50