Unable to predict test dataset : I have a training data set with the below variables

> str(PairsTrain)
            'data.frame':	1495698 obs. of  4 variables:
            $ itemID_1        : int  1 4 8 12 15 19 20 20 22 26 ...
            $ itemID_2        : int  4112648 1223296 2161930 5637025 113701         
            $ isDuplicate     : int  1 0 1 0 0 0 0 0 1 0 ...
            $ generationMethod: int  1 1 1 1 1 1 1 1 1 1 ...

I have learned from this dataset using the logistic regression glm() function 

    `mod1 <- glm(isDuplicate ~., data = PairsTrain, family = binomial)`

Below is the structure of m y test dataset

> str(Test)
       'data.frame':	1044196 obs. of  3 variables:
         $ id      : int  0 1 2 3 4 5 6 7 8 9 ...
         $ itemID_1: int  5 5 6 11 23 23 30 31 36 47 ...
         $ itemID_2: int  4670875 787210 1705280 3020777 5316130 3394969 2922567       

I am trying to make predictions on my test data set like below 

> `PredTest <- predict(mod1, newdata = Test, type = "response")`
Error in eval(expr, envir, enclos) : object 'generationMethod' not found

I get the above error, I am thinking that the reason for the error I am getting is the number of features in my test dataset which doesn't match the training dataset. I am not sure if I am correct and I am stuck here and don't know how to deal this situation. Any help is much appreciated.

//Sam