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Logistic regression classification table in R

I have a data set consisting of a dichotomous variable ( Y) and 12 independent variables ( X1to X12) stored in a csv file. Here are the first 5 rows of data:

Y,X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12  
0,9,3.86,111,126,14,13,1,7,7,0,M,46-50  
1,7074,3.88,232,4654,143,349,2,27,18,6,M,25-30  
1,5120,27.45,97,2924,298,324,3,56,21,0,M,31-35
1,18656,79.32,408,1648,303,8730,286,294,62,28,M,25-30
0,3869,21.23,260,2164,550,320,3,42,203,3,F,18-24

I built a logistic regression model from data using the following code:

mydata <- read.csv("data.csv")     
mylogit <- glm(Y~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10+X11+X12, data=mydata, 
               family="binomial")  
mysteps <- step(mylogit, Y~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10+X11+X12, data=mydata, 
                family="binomial")

I can get the predicted probabilities for each of the data using code:

theProbs <- fitted(mysteps)

Now I would like to create a classification table - using the first 20 rows of the data table ( mydata), from which I can determine the percentage of predicted probabilities that actually agree with the data. Note that for the dependent variable ( Y), 0 represents a probability that is less than 0.5, and 1 represents a probability that is greater than 0.5.

, . , - , .

+5
2

, , - , . xtabs

classDF <- data.frame(response = mydata$Y, predicted = round(fitted(mysteps),0))

xtabs(~ predicted + response, data = classDF)

:

           response
predicted   0   1
        0 339 126
        1 130 394
+8

, "" . ( (theProbs))

+1

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