Output of Logistic Regression Prediction I have created a Logistic Regression using the following code:
full.model.f = lm(Ft_45 ~ ., LOG_D)
base.model.f = lm(Ft_45 ~ IP_util_E2pl_m02_flg)
step(base.model.f, scope=list(upper=full.model.f, lower=~1),
     direction="forward", trace=FALSE)

I have then used the output to create a final model:
final.model.f = lm(Ft_45 ~ IP_util_E2pl_m02_flg + IP_util_E2_m02_flg + 
                           AE_NumVisit1_flg + OP_NumVisit1_m01_flg + IP_TotLoS_m02 + 
                           Ft1_45 + IP_util_E1_m05_flg + IP_TotPrNonElecLoS_m02 + 
                           IP_util_E2pl_m03_flg + LTC_coding + OP_NumVisit0105_m03_flg +
                           OP_NumVisit11pl_m03_flg + AE_ArrAmb_m02_flg)

Then I have predicted the outcomes for a different set of data using the predict function:
log.pred.f.v <- predict(final.model.f, newdata=LOG_V)

I have been able to use establish a pleasing ROC curve and created a table to establish the sensitivity and specificity which gives me responses I would expect. 
However What I am trying to do is establish for each row of data what the probability is of Ft_45 being 1. If I look at the output of log.pred.f.v I get, for example,:
1 -0.171739593    
2 -0.049905948    
3 0.141146419    
4 0.11615669    
5 0.07342591    
6 0.093054334    
7 0.957164383    
8 0.098415639    
.
.
.
104 0.196368229    
105 1.045208447    
106 1.05499112

As I only have a tentative grasp on what I am doing I am struggling to understand how to interpret the negative and higher that 1 values as I would expect a probability to be between 0 and 1.
So my question is am I just missing a step where I need to transform the output or have I gone completely wrong.
Thank you in advance for any help you are able to offer.
 A: First, it looks like you built a regular linear regression model, not a logistic regression model.  To build a logistic regression model, you need to use glm() with  family="binomial" , not lm().
Suppose you build the following logistic regression model using independent variables $x_1, x_2$, and $x_3$ to predict the probability of event $y$:
logit <- glm(y~x1+x2+x3,family="binomial")

This model has regression coefficients $\beta_0, \beta_1, \beta_2$ and $\beta_3$.  
If you then do predict(logit), R will calculate and return b0 + b1*x1 + b2*x2 + b3*x3.
Recall that your logistic regression equation is $y = log(\frac{p}{1-p}) = \beta_0 + \beta_1x_1 + \beta_2x_2 + \beta_3x_3$.  
So, to get the probabilities that you want, you need to solve this equation for $p$.
In R, you can do something like this:
pred <- predict(logit,newdata=data) #gives you b0 + b1x1 + b2x2 + b3x3
probs <- exp(pred)/(1+exp(pred)) #gives you probability that y=1 for each observation

A: Looking at the documentation of the predict.glm, seems that it as easy as using an extra parameter in predict call:
 type = "response"

See documentation:

type - the type of prediction required. The default is on the scale of
  the linear predictors; the alternative "response" is on the scale of
  the response variable. Thus for a default binomial model the default
  predictions are of log-odds (probabilities on logit scale) and type =
  "response" gives the predicted probabilities. The "terms" option
  returns a matrix giving the fitted values of each term in the model
  formula on the linear predictor scale. The value of this argument can
  be abbreviated

