# Missing Values NAs in the Test Data When using predict.lm in R

I have two data sets

1. Train data
2. Test data (with no dependent variable values but I have data on independent variable or you can say I need to forecast).

Using the training data (which has some NAs in the cell) I performed ordinary least square regression (OLS) using lm() in R and fitted the model & I got the $\beta$ coefficients of the regression model. (All is good so far!)

Now, in the process of prediction for the fitted values, I have some missing values for some cells in the test dataset. I used function predict() as follows:

 predict(ols, test_data.df, interval= "prediction", na.action=na.pass)


for the cell (or cells) with NA value the entire row is discarded in generating the output (yhat). Is there any function that could generate the yhat values (other than NAs) for the test data without discarding any rows with missing value in the cell.