How we can predict a Single Value Using a Regression Model in R

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To fit a linear regression model in R, we can use the lm() function, which uses the following syntax:

model <- lm(y ~ x1 + x2, data=df)

We can then use the following syntax to use the model to predict a single value:

predict(model, newdata = new)

The following examples show how to predict a single value using fitted regression models in R.

Example 1: Predict Using a Simple Linear Regression Model

The following code shows how to fit a simple linear regression model in R:

#create data
df <- data.frame(x=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),
                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))

#fit simple linear regression model
model <- lm(y ~ x, data=df)

And we can use the following code to predict the response value for a new observation:

#define new observation
new <- data.frame(x=c(5))

#use the fitted model to predict the value for the new observation
predict(model, newdata = new)

       1 
25.36364 

The model predicts that this new observation will have a response value of 25.36364.

Example 2: Predict Using a Multiple Linear Regression Model

The following code shows how to fit a multiple linear regression model in R:

#create data
df <- data.frame(x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),
                 x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),
                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))

#fit multiple linear regression model
model <- lm(y ~ x1 + x2, data=df)

And we can use the following code to predict the response value for a new observation:

#define new observation
new <- data.frame(x1=c(5),
                  x2=c(10))

#use the fitted model to predict the value for the new observation
predict(model, newdata = new)

       1 
26.17073 

The model predicts that this new observation will have a response value of 26.17073.

Potential Errors with Predicting New Values

The most common error you may run into when attempting to predict a new value is when the dataset you used to fit the regression model does not have the same column names as the new observation you’re attempting to predict.

For example, suppose we fit the following multiple linear regression model in R:

#create data
df <- data.frame(x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12),
                 x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14),
                 y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36))

#fit multiple linear regression model
model <- lm(y ~ x1 + x2, data=df)

Then suppose we attempt to use the model to predict the response value for this new observation:

#define new observation
new <- data.frame(x_1=c(5),
                  x_2=c(10))

#use the fitted model to predict the value for the new observation
predict(model, newdata = new)

Error in eval(predvars, data, env) : object 'x1' not found

We received an error because the column names for the new observation (x_1, x_2) do not match the column names of the original data frame (x1, x2) we used to fit the regression model.