upper scope has term ‘NA’ not included in model - r

Question

I am working on a data set and would like to do step wise logistic regression using some variables and to do so I am using the add1() function in R. A sample of the data set can be downloaded from the link here: https://drive.google.com/file/d/0B0N-Nc7kEi4bVjhDd1FDaEE5cEE/view?usp=sharing
I thereby fit a logistic regression using:
train <- read.csv('training.csv')
glm.model_step_1 <- glm(loan_status ~ acc_open_past_24mths + annual_inc + avg_cur_bal + bc_open_to_buy + delinq_2yrs + dti + inq_last_6mths + installment + int_rate + mo_sin_old_il_acct + mo_sin_old_rev_tl_op + mo_sin_rcnt_rev_tl_op + mo_sin_rcnt_tl + mort_acc + mths_since_last_delinq + mths_since_recent_bc + mths_since_recent_inq + num_accts_ever_120_pd + num_actv_bc_tl + num_actv_rev_tl + num_bc_tl + num_il_tl + num_op_rev_tl + num_tl_op_past_12m + pct_tl_nvr_dlq + percent_bc_gt_75 + pub_rec_bankruptcies + revol_bal + revol_util + term + total_acc + total_bc_limit + total_il_high_credit_limit + fico_mean + addr_state + emp_length + verification_status + Count_NA + Info_missing + Engineer + Teacher + Doctor + Professor + Manager + Director + Analyst + senior + lead + consultant + home_ownership_own + home_ownership_rent + purpose_debt_consolidation + purpose_medical + purpose_credit_card + purpose_other,
data = train,
family = binomial(link = 'logit'))
And use the add1() function to do a forward selection.
add1(glm.model_step_1, scope = train)
This code does not work. I get the below error:
Error in factor.scope(attr(terms1, "factors"), list(add = attr(terms2, :
upper scope has term ‘NA’ not included in model
Does anyone know how to solve this error?
A question asked previously on datascience.stackexchange (https://datascience.stackexchange.com/questions/11604/checking-regression-coefficients-stability) mentioned checking for NAs. There aren't any NAs in the data set and that can be confirmed by running sapply(train, function(x) sum(is.na(x))

Answer 1

The train dataset of #Jash Sash has some anomalous values inside which force read.csv to read some numerical variables as factors with many categories.
Anyway, I consider here a model with only few variables in order to show how to avoid the error message reported above.
Remember that the scope argument must be a "formula giving the terms to be considered for adding or dropping"; it cannot be a data.frame like in the code of #Jash Sash.
train <- read.csv('training.csv')
numeric <- apply(train,2,is.factor)
glm.model_step_1 <- glm(loan_status ~ acc_open_past_24mths + avg_cur_bal + bc_open_to_buy,
data = na.omit(train),
family = binomial(link = 'logit'))
add1(glm.model_step_1, scope=~.+delinq_2yrs+inq_last_6mths+int_rate)
The results is:
Model:
loan_status ~ acc_open_past_24mths + avg_cur_bal + bc_open_to_buy
Df Deviance AIC
<none> 1038.6 1046.6
delinq_2yrs 1 1037.9 1047.9
inq_last_6mths 1 1038.0 1048.0
int_rate 1 1038.0 1048.0