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The **glm()** function in R can be used to fit generalized linear models. This function is particularly useful for fitting logistic regression models, Poisson regression models, and other complex models.

Once we’ve fit a model, we can then use the **predict()** function to predict the response value of a new observation.

This function uses the following syntax:

**predict(object, newdata, type=”response”)**

where:

**object:**The name of the model fit using the glm() function**newdata:**The name of the new data frame to make predictions for**type:**The type of prediction to make.

The following example shows how to fit a generalized linear model in R and how to then use the model to predict the response value of a new observation it hasn’t seen before.

**Example: Using the predict function with glm in R**

For this example, we’ll use the built-in R dataset called **mtcars**:

#view first six rows ofmtcarsdata frame head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

We’ll fit the following logistic regression model in which we use the variables **disp** and **hp** to predict the response variable **am** (the transmission type of the car: 0 = automatic, 1 = manual).

#fit logistic regression model model #view model summary summary(model) Call: glm(formula = am ~ disp + hp, family = binomial, data = mtcars) Deviance Residuals: Min 1Q Median 3Q Max -1.9665 -0.3090 -0.0017 0.3934 1.3682 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.40342 1.36757 1.026 0.3048 disp -0.09518 0.04800 -1.983 0.0474 * hp 0.12170 0.06777 1.796 0.0725 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 43.230 on 31 degrees of freedom Residual deviance: 16.713 on 29 degrees of freedom AIC: 22.713 Number of Fisher Scoring iterations: 8

We can then use this model to predict the probability that a new car has an automatic transmission (am=0) or a manual transmission (am=1) by using the following code:

#define new observation newdata = data.frame(disp=200, hp= 100) #use model to predict value of am predict(model, newdata, type="response") 1 0.00422564

The model predicts the probability of the new car having a manual transmission (am=1) to be **0.004**. This means it’s highly like that this new car has an automatic transmission.

Note that we can also make several predictions at once if we have a data frame that has multiple new cars.

For example, the following code shows how to use the fitted model to predict the probability of a manual transmission for three new cars:

#define new data frame of three cars newdata = data.frame(disp=c(200, 180, 160), hp=c(100, 90, 108)) #view data frame newdata disp hp 1 200 100 2 180 90 3 160 108 #use model to predict value ofamfor all three cars predict(model, newdata, type="response") 1 2 3 0.004225640 0.008361069 0.335916069

Here’s how to interpret the output:

- The probability that car 1 has a manual transmission is
**.004**. - The probability that car 2 has a manual transmission is
**.008**. - The probability that car 3 has a manual transmission is
**.336**.

**Notes**

The names of the columns in the new data frame should exactly match the names of the columns in the data frame that were used to build the model.

Notice that in our previous example, the data frame we used to build the model contained the following column names for our predictor variables:

**disp****hp**

Thus, when we created the new data frame called **newdata** we made sure to also name the columns:

**disp****hp**

If the names of the columns do not match, you’ll receive the following error message:

**Error in eval(predvars, data, env) **

Keep this in mind when using the predict() function.

**Additional Resources**

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Perform Polynomial Regression in R

How to Create a Prediction Interval in R